Search

Your search keyword '"YOLO"' showing total 3,415 results
Start Over Descriptor "YOLO"
3,415 results on '"YOLO"'

51. Comparative Evaluation of Convolutional Neural Network Object Detection Algorithms for Vehicle Detection.

Author

Reddy, Saieshan, Pillay, Nelendran, and Singh, Navin

Subjects
OBJECT recognition (Computer vision), CONVOLUTIONAL neural networks, COMPUTER vision, VISUAL fields, DETECTORS
Abstract

The domain of object detection was revolutionized with the introduction of Convolutional Neural Networks (CNNs) in the field of computer vision. This article aims to explore the architectural intricacies, methodological differences, and performance characteristics of three CNN-based object detection algorithms, namely Faster Region-Based Convolutional Network (R-CNN), You Only Look Once v3 (YOLO), and Single Shot MultiBox Detector (SSD) in the specific domain application of vehicle detection. The findings of this study indicate that the SSD object detection algorithm outperforms the other approaches in terms of both performance and processing speeds. The Faster R-CNN approach detected objects in images with an average speed of 5.1 s, achieving a mean average precision of 0.76 and an average loss of 0.467. YOLO v3 detected objects with an average speed of 1.16 s, achieving a mean average precision of 0.81 with an average loss of 1.183. In contrast, SSD detected objects with an average speed of 0.5 s, exhibiting the highest mean average precision of 0.92 despite having a higher average loss of 2.625. Notably, all three object detectors achieved an accuracy exceeding 99%. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

52. A Neural-Network-Based Cost-Effective Method for Initial Weld Point Extraction from 2D Images.

Author

Lopez-Fuster, Miguel-Angel, Morgado-Estevez, Arturo, Diaz-Cano, Ignacio, and Badesa, Francisco J.

Subjects
ROBOTIC welding, OBJECT recognition (Computer vision), COMPUTER vision, WELDING, DEEP learning
Abstract

This paper presents a novel approach for extracting 3D weld point information using a two-stage deep learning pipeline based on readily available 2D RGB cameras. Our method utilizes YOLOv8s for object detection, specifically targeting vertices, followed by semantic segmentation for precise pixel localization. This pipeline addresses the challenges posed by low-contrast images and complex geometries, significantly reducing costs compared with traditional 3D-based solutions. We demonstrated the effectiveness of our approach through a comparison with a 3D-point-cloud-based method, showcasing the potential for improved speed and efficiency. This research advances the field of automated welding by providing a cost-effective and versatile solution for extracting key information from 2D images. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

53. FOREST FIRE DETECTION BASED ON IMPROVED YOLOV7 MODELING.

Author

YANG, Q., ZHANG, T., TONG, X., and HU, L. H.

Subjects
FOREST fires, DEEP learning, ALGORITHMS, SPEED
Abstract

An improved forest fire detection algorithm based on the You Only Look Once v7 (YOLOv7) is proposed in this paper to address the poor accuracy and speed of traditional forest fire detection. In order to guide this model to better focus on the features of forest smoke and fire, this paper introduces the Convolutional Block Attention Module (CBAM) channel attention mechanism and spatial attention mechanism in the backbone network and the head of the YOLOv7 model. It uses the SCYLLA-IoU (SIoU) loss function to replace the Complete-IoU (CIoU) loss function in order to improve the regression accuracy and convergence speed. Experiments show that the improved YOLOv7 algorithm achieves 78.8%, 65.4%, and 70.8% accuracy, recall, and average accuracy, respectively. Compared with the original algorithm, the accuracy, recall, and average accuracy are improved by 4.7%, 1.6% and 5.3%. The algorithm reduces missed detections, improves the accuracy and practicality of forest fire detection, and has good robustness in different complex scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

54. Towards real-time video analysis of flooded areas: redundancy-based accelerator for object detection models.

Author

AV, Shubhasree, Sankaran, Praveen, and C.V, Raghu

Abstract

The state of Kerala in India has seen multiple instances of intense cyclones in recent years, resulting in heavy flooding. One of the biggest challenges faced by rescuers is the accessibility to flooded areas and buildings during rescue operations. In such scenarios, unmanned aerial vehicles (UAVs) can deliver reliable aerial visual data to aid planning and operations during rescue. Object detectors based on deep learning methods provide an effective solution to automate the process of detecting relevant information from image/video data. These models are complex and resource-hungry, leading to severe speed constraints during field operations. The pixel displacement algorithm (PDA), a portable and effective technique, is developed in this work to speed up object detection models on devices with limited resources, such as edge devices. This method can be integrated with all object detection models to speed up the inference time. The proposed method is combined with multiple object detection models in this work to show its effectiveness. The YOLOv4 model combined with the proposed method outperformed the AP50 performance of the YOLOv4-tiny model by 6 % while maintaining the same processing time. This approach gave almost 10 × speed improvement to Jetson Nano at an accuracy cost of 3 % when compared to YOLOv4. Further, a model to predict maximum pixel shift with respect to frame skip is proposed using parameters such as the altitude and velocity of the UAV and the tilt of the camera. Accurate prediction of pixel shift leads to a reduced search area, leading to reduced inference time. The effectiveness of the proposed model was tested against annotated locations, and it was found that the method was able to predict the search area for each test video segment with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

55. ARF-YOLOv8: a novel real-time object detection model for UAV-captured images detection.

Author

Zeng, YaLin, Guo, DongJin, He, WeiKai, Zhang, Tian, and Liu, ZhongTao

Abstract

There are several difficulties in the task of object detection for Unmanned Aerial Vehicle (UAV) photography images, including the small size of objects, densely distributed objects, and diverse perspectives from which the objects are captured. To tackle these challenges, we proposed a real-time algorithm named adjusting overall receptive field enhancement YOLOv8 (ARF-YOLOv8) for object detection in UAV-captured images. Our approach begins with a comprehensive restructuring of the YOLOv8 network architecture. The primary objectives are to mitigate the loss of shallow-level information and establish an optimal model receptive field. Subsequently, we designed a bibranch fusion attention module based on Coordinate Attention which is seamlessly integrated into the detection network. This module combines features processed by Coordinate Attention module with shallow-level features, facilitating the extraction of multi-level feature information. Furthermore, recognizing the influence of target size on boundary box loss, we refine the boundary box loss function CIoU Loss employed in YOLOv8. Extensive experimentation conducted on the visdrone2019 dataset provides empirical evidence supporting the superior performance of ARF-YOLOv8. In comparison to YOLOv8, our method demonstrates a noteworthy 6.86% increase in mAP (0.5:0.95) while maintaining similar detection speeds. The code is available at . [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

56. Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment.

Author

Balasubramani, Madankumar, Sung, Chih-Wei, Hsieh, Mu-Yang, Huang, Edward Pei-Chuan, Shieh, Jiann-Shing, and Abbod, Maysam F.

Subjects
DEEP learning, IMAGE segmentation, IMAGE analysis, FEATURE extraction, COMPUTER-assisted image analysis (Medicine), ECHOCARDIOGRAPHY
Abstract

Accurate segmentation of the left ventricle (LV) using echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing a YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed–accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO-based model on a well-annotated Echo image dataset, we can leverage the model's strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31% and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

57. Color Histogram Contouring: A New Training-Less Approach to Object Detection.

Author

Rabie, Tamer, Baziyad, Mohammed, Sani, Radhwan, Bonny, Talal, and Fareh, Raouf

Subjects
OBJECT recognition (Computer vision), COMPUTER vision, HISTOGRAMS, AUTONOMOUS robots, BIN packing problem, COLOR, MOBILE robots
Abstract

This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise information in chrominance features without increasing bin sizes, which can lead to false detections. This feature vector demonstrates invariance to lighting changes and is designed to mimic the opponent color axes used by the human visual system. The proposed CHC algorithm iterates over non-zero histogram bins of unique color features in the model, creating a feature vector for each, and emphasizes those matching in both the scene and model histograms. When both model and scene histograms for these unique features align, it ensures the presence of the model in the scene image. Extensive experiments across various scenarios show that the proposed CHC technique outperforms the benchmark training-less Swain and Ballard method and the algorithm of Viola and Jones. Additionally, a comparative experiment with the state-of-the-art You Only Look Once (YOLO) technique reveals that the proposed CHC technique surpasses YOLO in scenarios with limited training data, highlighting a significant advancement in training-less object detection. This approach offers a valuable addition to computer vision, providing an effective training-less solution for real-time autonomous robot localization and mapping in unknown environments. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

58. Deep learning approach for detecting tomato flowers and buds in greenhouses on 3P2R gantry robot

Author

Rajmeet Singh, Asim Khan, Lakmal Seneviratne, and Irfan Hussain

Subjects
Tomato flower, Bud, Deep learning, YOLO, Transfer learning, Pollination robot, Medicine, Science
Abstract

Abstract In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and robotics for pollinating flowers. Robotic pollination offers several benefits, including reduced labor requirements and preservation of costly pollen through artificial tomato pollination. However, previous studies have primarily focused on the labeling and detection of tomato flowers alone. Therefore, the objective of this study was to develop a comprehensive methodology for simultaneously labeling, training, and detecting tomato flowers specifically tailored for robotic pollination. To achieve this, transfer learning techniques were employed using well-known models, namely YOLOv5 and the recently introduced YOLOv8, for tomato flower detection. The performance of both models was evaluated using the same image dataset, and a comparison was made based on their Average Precision (AP) scores to determine the superior model. The results indicated that YOLOv8 achieved a higher mean AP (mAP) of 92.6% in tomato flower and bud detection, outperforming YOLOv5 with 91.2%. Notably, YOLOv8 also demonstrated an inference speed of 0.7 ms when considering an image size of $$1920 \times 1080$$ 1920 × 1080 pixels resized to $$640 \times 640$$ 640 × 640 pixels during detection. The image dataset was acquired during both morning and evening periods to minimize the impact of lighting conditions on the detection model. These findings highlight the potential of YOLOv8 for real-time detection of tomato flowers and buds, enabling further estimation of flower blooming peaks and facilitating robotic pollination. In the context of robotic pollination, the study also focuses on the deployment of the proposed detection model on the 3P2R gantry robot. The study introduces a kinematic model and a modified circuit for the gantry robot. The position-based visual servoing method is employed to approach the detected flower during the pollination process. The effectiveness of the proposed visual servoing approach is validated in both un-clustered and clustered plant environments in the laboratory setting. Additionally, this study provides valuable theoretical and practical insights for specialists in the field of greenhouse systems, particularly in the design of flower detection algorithms using computer vision and its deployment in robotic systems used in greenhouses.

Published
2024
Full Text
View/download PDF

59. Small Object Detection in UAV Images Based on YOLOv8n

Author

LongYan Xu, YiFan Zhao, YaHong Zhai, LiMing Huang, and ChongWei Ruan

Subjects
Small object detection, UAV vision, YOLO, Wise-IoU, Conditionally parameterized convolutions, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract With the rapid development of unmanned aerial vehicle (UAV) technology, there is an urgent need for high-performance aerial object detection algorithms that are tailored for deployment on drones with limited computing capabilities. This paper proposes a series of improvements to the state-of-the-art YOLOv8 object detector to enhance its detection accuracy and speed for small, partially occluded objects in complex environments. Specifically, we introduce multi-scale feature fusion through additional detection layers, employ conditionally parameterized convolutions to increase representational capacity, and import a dynamic non-monotonic loss function named Wise-IoU to enable more effective regression of bounding boxes. Experiments conducted on the large-scale UAV benchmark dataset VisDrone demonstrate that the improved model achieves state-of-the-art accuracy of 37.6% mAP with 3 M parameters, outperforming other lightweight YOLO detectors and two-stage detectors like Faster R-CNN. The improved model also reaches 40 FPS on an embedded edge device, validating its efficiency and suitability for real-time UAV applications. Through comprehensive quantitative experiments and visual results, this work provides valuable insights and techniques to tailor object detection algorithms for robust and efficient deployment on UAVs with limited onboard computing power.

Published
2024
Full Text
View/download PDF

60. Segmentation and characterization of macerated fibers and vessels using deep learning

Author

Saqib Qamar, Abu Imran Baba, Stéphane Verger, and Magnus Andersson

Subjects
Instance segmentation, YOLO, Wood, Fibers, Optical microscopy, Plant culture, SB1-1110, Biology (General), QH301-705.5
Abstract

Abstract Purpose Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells’ shape, size, and arrangement in microscopy images is crucial for understanding wood characteristics. Typically, this involves macerating (soaking) samples in a solution to separate cells, then spreading them on slides for imaging with a microscope that covers a wide area, capturing thousands of cells. However, these cells often cluster and overlap in images, making the segmentation difficult and time-consuming using standard image-processing methods. Results In this work, we developed an automatic deep learning segmentation approach that utilizes the one-stage YOLOv8 model for fast and accurate segmentation and characterization of macerated fiber and vessel form aspen trees in microscopy images. The model can analyze 32,640 x 25,920 pixels images and demonstrate effective cell detection and segmentation, achieving a $$\hbox {mAP}_{0.5-0.95}$$ mAP 0.5 - 0.95 of 78 %. To assess the model’s robustness, we examined fibers from a genetically modified tree line known for longer fibers. The outcomes were comparable to previous manual measurements. Additionally, we created a user-friendly web application for image analysis and provided the code for use on Google Colab. Conclusion By leveraging YOLOv8’s advances, this work provides a deep learning solution to enable efficient quantification and analysis of wood cells suitable for practical applications.

Published
2024
Full Text
View/download PDF

61. Deep learning based identification and tracking of railway bogie parts

Author

Muhammad Zakir Shaikh, Zeeshan Ahmed, Enrique Nava Baro, Samreen Hussain, and Mariofanna Milanova

Subjects
Computer Vision, Object Detection, Deep Learning, Yolo, Train Rolling Stock, Wheelset, Engineering (General). Civil engineering (General), TA1-2040
Abstract

The Train Rolling-Stock Examination (TRSE) is a safety examination process that physically examines the bogie parts of a moving train, typically at speeds over 30 km/h. Currently, this inspection process is done manually by railway personnel in many countries to ensure safety and prevent interruptions to rail services. Although many earlier attempts have been made to semi-automate this process through computer-vision models, these models are iterative and still require manual intervention. Consequently, these attempts were unsuitable for real-time implementations. In this work, we propose a detection model by utilizing a deep-learning based classifier that can precisely identify bogie parts in real-time without manual intervention, allowing an increase in the deployability of these inspection systems. We implemented the Anchor-Free Yolov8 (AFYv8) model, which has a decoupled-head module for recognizing bogie parts. Additionally, we incorporated bogie parts tracking with the AFYv8 model to gather information about any missing parts. To test the effectiveness of the AFYv8-model, the bogie videos were captured at three different timestamps and the result shows the increase in the recognition accuracy of TRSE by 10 % compared to the previously developed classifiers. This research has the potential to enhance railway safety and minimize operational interruptions.

Published
2024
Full Text
View/download PDF

62. Enhanced Horse Stable Security Monitoring Using Deep Learning: Investigating YOLO Techniques and Architecture

Author

Ashifah, Nurul Alea, Shaharum, Syamimi Mardiah, Yakno, Marlina, Samsudin, W. S. W., Faudzi, A. A. M., Dom, Paiza Md, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Md. Zain, Zainah, editor, Sulaiman, Norizam, editor, Mustafa, Mahfuzah, editor, Shakib, Mohammed Nazmus, editor, and A. Jabbar, Waheb, editor

Published
2024
Full Text
View/download PDF

65. Interactive Neural Network for Object Detection in YOLOv5 and YOLOv8

Author

Taskin, Elif Melis, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
Full Text
View/download PDF

66. Object Detection Using YOLOv4 and PSO-CNN

Author

Arulalan, V., Javali, Thatiparthy, Jha, Shivani, Garg, Parth, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
Full Text
View/download PDF

67. Forest Fire Surveillance Through Deep Learning Segmentation and Drone Technology

Author

Yandouzi, Mimoun, Boukricha, Sokaina, Grari, Mounir, Berrahal, Mohammed, Moussaoui, Omar, Azizi, Mostafa, Ghoumid, Kamal, Elmiad, Aissa Kerkour, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Serrhini, Mohammed, editor, and Ghoumid, Kamal, editor

Published
2024
Full Text
View/download PDF

68. Designing a Student Administration System Utilizing the YOLO-Based Model

Author

Vinh, Phan Van, Thanh, Truong Le My, Dat, Huynh Thanh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pagac, Marek, editor, Hajnys, Jiri, editor, Kozior, Tomasz, editor, Nguyen, Hoang-Sy, editor, Nguyen, Van Dung, editor, and Nag, Akash, editor

Published
2024
Full Text
View/download PDF

69. An Exploration of Object Detection and Vehicular Communication for Autonomous Vehicles

Author

Padmavathi, A., Pullela, Dheeraj Reddy, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Owoc, Mieczyslaw Lech, editor, Varghese Sicily, Felix Enigo, editor, Rajaram, Kanchana, editor, and Balasundaram, Prabavathy, editor

Published
2024
Full Text
View/download PDF

70. Leveraging YOLO for Real-Time Video Analysis of Animal Welfare in Pig Slaughtering Processes

Author

Beecks, Christian, Amalraj, Anandraj, Graß, Alexander, Jentsch, Marc, Kitschke, Felix, Norz, Maximilian, Schäffer, Patric, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hotho, Andreas, editor, and Rudolph, Sebastian, editor

Published
2024
Full Text
View/download PDF

71. YOLOv3 for Brain Tumor Detection

Author

Chefrour, Aida, Drissi, Samia, Kechaichia, Narimene, Haou, Hadjer, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Laouar, Mohamed Ridda, editor, Balas, Valentina Emilia, editor, Piuri, Vincenzo, editor, Rad, Dana, editor, Touati Hamad, Zineb, editor, and Cheddad, Abbas, editor

Published
2024
Full Text
View/download PDF

72. Deep Detection Dreams: Enhancing Visualization Tools for Single Stage Object Detectors

Author

Limberg, Christian, Harter, Augustin, Melnik, Andrew, Ritter, Helge, Prendinger, Helmut, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, de Sousa, A. Augusto, editor, Bashford-Rogers, Thomas, editor, Paljic, Alexis, editor, Ziat, Mounia, editor, Hurter, Christophe, editor, Purchase, Helen, editor, Radeva, Petia, editor, Farinella, Giovanni Maria, editor, and Bouatouch, Kadi, editor

Published
2024
Full Text
View/download PDF

73. A Review on YOLO Algorithms for Social Distancing

Author

Kumar, Vijay, Murmu, Mahendra Kumar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pastor-Escuredo, David, editor, Brigui, Imene, editor, Kesswani, Nishtha, editor, Bordoloi, Sushanta, editor, and Ray, Ashok Kumar, editor

Published
2024
Full Text
View/download PDF

74. AI-Enhanced Big Data IoT System for Helmet Detection to Boost Biker Safety

Author

Farhaoui, Yousef, El Allaoui, Ahmad, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, and Farhaoui, Yousef, editor

Published
2024
Full Text
View/download PDF

76. Object Detection in Movies – Case Study

Author

Nurhasan, Amnaduny Akhara, Kasprowski, Pawel, Harezlak, Katarzyna, Birawo, Birtukan Adamu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Chbeir, Richard, editor, Manolopoulos, Yannis, editor, Fujita, Hamido, editor, Hong, Tzung-Pei, editor, Nguyen, Le Minh, editor, and Wojtkiewicz, Krystian, editor

Published
2024
Full Text
View/download PDF

77. Lightweight Coal Flow Foreign Object Detection Algorithm

Author

Nie, Ru, Shen, Xiaobing, Li, Zhengwei, Jiang, Yanxia, Liao, Hongmei, You, Zhuhong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Si, Zhanjun, editor, and Pan, Yijie, editor

Published
2024
Full Text
View/download PDF

78. Application of Unmanned Aerial Vehicles for Autonomous Fire Detection

Author

Silva, José, Sousa, David, Vaz, Paulo, Martins, Pedro, López-Rivero, Alfonso, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, de la Iglesia, Daniel H., editor, de Paz Santana, Juan F., editor, and López Rivero, Alfonso J., editor

Published
2024
Full Text
View/download PDF

79. Smoke Emission Detection by Deep Learning

Author

Lee, Jo-Huan, Cheng, Chi-Bin, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ansari, Jonathan, editor, Fuchs, Sebastian, editor, Trutschnig, Wolfgang, editor, Lubiano, María Asunción, editor, Gil, María Ángeles, editor, Grzegorzewski, Przemyslaw, editor, and Hryniewicz, Olgierd, editor

Published
2024
Full Text
View/download PDF

80. Real-Time AI-Based Face-Mask Detection

Author

Sawant, Prathamesh, Bokefode, Sagar, Deshmukh, Shrikant, Aswathy, MA, Yadav, Vipin P., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Umakanta, editor, Tripathy, Asis Kumar, editor, Sahoo, Jyoti Prakash, editor, Sarkar, Mahasweta, editor, and Li, Kuan-Ching, editor

Published
2024
Full Text
View/download PDF

81. Deep Dive into Invoice Intelligence: A Benchmark Study of Leading Models for Automated Invoice Data Extraction

Author

Bajrami, Merxhan, Ackovska, Nevena, Stojkoska, Biljana, Lameski, Petre, Zdravevski, Eftim, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2024
Full Text
View/download PDF

82. Performance Comparison in Traffic Sign Recognition Using Deep Learning

Author

Dang Hai, Binh, Nguyen, Huu Duc, Vo, Thanh Nam, Tran, Phuong-Nam, Nguyen, Cuong Tuan, Dang, Duc Ngoc Minh, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Vo, Nguyen-Son, editor, Ha, Dac-Binh, editor, and Jung, Haejoon, editor

Published
2024
Full Text
View/download PDF

84. Automated System For Chromosome Karyotyping Detection

Author

Naidu, Yannam Dimple Tunvey, Yaragani, Supriya, Mounika, Tirumala, Vasam, Vamsi, Mutyala, Swathi, Fournier-Viger, Philippe, Series Editor, Madhavi, K. Reddy, editor, Subba Rao, P., editor, Avanija, J., editor, Manikyamba, I. Lakshmi, editor, and Unhelkar, Bhuvan, editor

Published
2024
Full Text
View/download PDF

85. ISE-UFDS: A Dataset for Detecting the Degree of Danger to Vehicles in Urban Flooding and Performance Assessment

Author

Sun, Jiwu, Zhang, Cheng, Xu, Cheng, Wang, Pengfei, Liu, Hongzhe, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Chuanlei, editor, and Zhang, Qinhu, editor

Published
2024
Full Text
View/download PDF

86. A Framework to Detect Social Distancing Violation and Mask Use in Public Places

Author

Nagdeote, Sushma, Singh, Simrandeep, kalambe, Aditya, Gomes, Aaron, Bhupatwar, Amardeep, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pant, Millie, editor, Deep, Kusum, editor, and Nagar, Atulya, editor

Published
2024
Full Text
View/download PDF

87. Pothole Detection and Prediction Using Deep Learning with CNN and YOLOv8

Author

Swami, Dinesh, Jangid, Mahesh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Rajesh, editor, Verma, Ajit Kumar, editor, Verma, Om Prakash, editor, and Wadehra, Tanu, editor

Published
2024
Full Text
View/download PDF

88. Parallel Deep Convolution Neural Network (P-DCNN) Prediction of Paddy Crop Disease

Author

Gangadevi, G., Raja Ratna, S., Jospin Jeya, J., Priya, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Rajesh, editor, Verma, Ajit Kumar, editor, Verma, Om Prakash, editor, and Wadehra, Tanu, editor

Published
2024
Full Text
View/download PDF

91. Automatic Tracking of Marine Life by Multirotor Type UAV Using Image Recognition and Deep Neural Network

Author

Arai, Kyoma, Koitabashi, Kaito, Yoshida, Yayoi, Inada, Yoshinobu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
Full Text
View/download PDF

92. Enhancing Video Surveillance with Deep Learning-Based Real-Time Handgun Detection and Tracking

Author

Yadav, Pavinder, Gupta, Nidhi, Sharma, Pawan Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kaur, Harkeerat, editor, Jakhetiya, Vinit, editor, Goyal, Puneet, editor, Khanna, Pritee, editor, Raman, Balasubramanian, editor, and Kumar, Sanjeev, editor

Published
2024
Full Text
View/download PDF

93. LiteFace: A Light-Weight Multi-person Face Detection Model

Author

Anand, Kanupriya, Murali, Nirmala, Mishra, Deepak, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kaur, Harkeerat, editor, Jakhetiya, Vinit, editor, Goyal, Puneet, editor, Khanna, Pritee, editor, Raman, Balasubramanian, editor, and Kumar, Sanjeev, editor

Published
2024
Full Text
View/download PDF

94. GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation

Author

Gong, Jingzhi, Li, Sisi, d’Aloisio, Giordano, Ding, Zishuo, Ye, Yulong, Langdon, William B., Sarro, Federica, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jahangirova, Gunel, editor, and Khomh, Foutse, editor

Published
2024
Full Text
View/download PDF

96. Handwritten Equation Detection in Disconnected, Low-Cost Mobile Devices

Author

Souza, Everton, Santos, Ermesson L. dos, Rodrigues, Luiz, Rosa, Daniel, Cordeiro, Filipe, Pereira, Cicero, Chevtchenko, Sergio, Carvalho, Ruan, Vieira, Thales, Marinho, Marcelo, Dermeval, Diego, Bittencourt, Ig Ibert, Isotani, Seiji, Macario, Valmir, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Olney, Andrew M., editor, Chounta, Irene-Angelica, editor, Liu, Zitao, editor, Santos, Olga C., editor, and Bittencourt, Ig Ibert, editor

Published
2024
Full Text
View/download PDF

97. Automatic Detection of Polyps Using Deep Learning

Author

Oliveira, Francisco, Barbosa, Dalila, Paçal, Ishak, Leite, Danilo, Cunha, António, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Cunha, António, editor, Paiva, Anselmo, editor, and Pereira, Sandra, editor

Published
2024
Full Text
View/download PDF

99. Advancing Agricultural Sustainability Through an AI Powered Classification Framework of Plant Pests and Diseases

Author

Hasan, Hadi, Kakoun, Razan Al, Massaad, Gaby, Awad, Mariette, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Karydis, Ioannis, editor, Papaleonidas, Antonios, editor, and Chochliouros, Ioannis, editor

Published
2024
Full Text
View/download PDF

100. Towards Robust Road Quality Detection Using Different Detection Models

Author

Thantharate, Aayushi Vinod, Goodwin, Morten, Andersen, Per-Arne, Gupta, Aditya, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results