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In view of the low segmentation accuracy for small-scale object and insufficient segmentation of local boundary for semantic segmentation methods based on Deep Learning, this paper proposes an image semantic segmentation approach based on attention mechanism and feature fusion. On the basis of ensuring the overall accuracy, the segmentation accuracy of small-scale object and local boundary is improved, and it meets the requirement of accurately segmenting the object in the complex background. Firstly, an image semantic segmentation model based on hybrid cascade and feature fusion is proposed, and the hybrid concatenation and multi-cores pooling methods are used to extract deeper semantic information. Then, a cross-stages fusion approach is designed to divide the backbone network of the encoder stage in the network and the improved Atrous Spatial Pyramid Pooling module into three stages to fully utilize the different semantic information of the shallow and deep layers. Thirdly, the attention mechanism is introduced into the hybrid cascade and feature fusion image semantic segmentation network model, and image semantic segmentation model based on cross-stages and attention mechanisms is explained. Self attention is added to channel attention enhances the connection between feature maps, and one-dimensional convolution in the spatial attention mechanism is used to increase the spatial receptive field. The final results on the public dataset PASCAL VOC2012 and SUIM show that MIoUs have reached 86.68% and 61.55% respectively, and it proved that the overall accuracy of the approach proposed in this paper is higher than other ones.

This work reviews the critical challenge of data scarcity in developing Transformer-based models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), specifically focusing on Motor Imagery (MI) decoding. While EEG-BCIs hold immense promise for applications in communication, rehabilitation, and human-computer interaction, limited data availability hinders the use of advanced deep-learning models such as Transformers. In particular, this paper comprehensively analyzes three key strategies to address data scarcity: data augmentation, transfer learning, and the inherent attention mechanisms of Transformers. Data augmentation techniques artificially expand datasets, enhancing model generalizability by exposing them to a wider range of signal patterns. Transfer learning utilizes pre-trained models from related domains, leveraging their learned knowledge to overcome the limitations of small EEG datasets. By thoroughly reviewing current research and methodologies, this work underscores the importance of these strategies in overcoming data scarcity. It critically examines the limitations imposed by limited datasets and showcases potential solutions being developed to address these challenges. This comprehensive survey, focusing on the intersection of data scarcity and technological advancements, aims to provide a critical analysis of the current state-of-the-art in EEG-BCI development. By identifying research gaps and suggesting future directions, the paper encourages further exploration and innovation in this field. Ultimately, this work aims to contribute to the advancement of more accessible, efficient, and accurate EEG-BCI systems by addressing the fundamental challenge of data scarcity.

Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects in visual streams presents distinct challenges. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Through tailored preprocessing and architectural adjustments, we heighten the model’s sensitivity to object movements. Rigorous testing against KITTI, LASIESTA, PESMOD, and MOCS benchmark datasets revealed that the modified YOLOv8 outperforms the state-of-the-art detection models, especially in environments with significant movement. Specifically, our model achieved an accuracy of 90%, a mean Average Precision (mAP) of 90%, and maintained a processing speed of 30 frames per second (FPS), with an Intersection over Union (IoU) score of 80%. This paper offers a detailed insight into object trajectories, proving invaluable in areas like security, traffic management, and film analysis where motion understanding is critical. As the importance of dynamic scene interpretation grows in artificial intelligence and computer vision, the proposed enhanced YOLOv8 detection model highlights the potential of specialized object detection and underscores the significance of our findings in the evolving field of object detection.

The real-time analysis of gas flares is one of the most challenging problems in the operation of various combustion-involving industries, such as oil and gas refineries. Despite the crucial role of gas flares in securing safe plant operation and lowering environmental pollution, they are among the least monitored components of petrochemical plants due to their harsh working environments. Remote sensing techniques are emerging as potential alternatives for conventional sampling-based techniques in visual inspection and performance analysis. This paper presents an in-depth review of significant achievements in Gas flares monitoring over past two decades and highlights ongoing challenges in Artificial Intelligence (AI)-enhanced remote inspection. By reading the content, both industry professionals and academic researchers can gain a comprehensive knowledge of improvements from the integration of AI and remote sensing, understanding the trend of Gas flares monitoring. The paper commences with an analysis of RGB camera-based methods, focusing on how their combination with cutting-edge machine learning and deep learning algorithms can significantly improve the detection, segmentation, and measurement of gas flare systems. It then explores the use of hyper-spectral imaging techniques, including infrared cameras and space-borne satellite sensors, underscoring their potential in remote monitoring and performance analysis. Additionally, the effectiveness of multi-view inspection methods is assessed, highlighting how these approaches enhance the monitoring capabilities. Finally, the paper identifies key research areas that require further attention. It also presents a clearer direction for future progress, emphasizing the importance of continuous research to foster advancements and facilitate broader commercial adoption.

Speech and voice disorders are highly prevalent and a significant concern, particularly among children. These disorders have a notable impact on individual’s personalities, academic performance and overall development. Early detection and intervention of these disorders surely help out and generate an effective outcome. Automatic detection of speech and voice disorders at an early stage plays a crucial role in identifying and addressing these communication challenges because of their efficiency over traditional diagnostic methods. Over time, numerous techniques were developed in the past, but some of them have become less efficient in the present scenario. Deep Learning has surfaced as a recent and significant advancement in detecting speech and voice disorders. This paper provides a systematic review of the utilization of deep learning techniques for the detection of speech and voice disorders. The review encompasses studies published from 2018 to 2023 exploring various architectures of deep learning models for capturing complex patterns in speech data. Each deep learning based speech sound disorder detection technique is discussed with critical appraisal and relevant benchmarks available for evaluation of results. This review holds significance for new researchers who are interested in exploring the field of automatic speech disorder detection as the paper concludes by discussing future directions and potential areas of improvement in automatic speech sound disorder detection using deep learning.

Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

This paper aims to develop a defect-background separated generative adversarial network (GAN) using deep learning and GAN to enhance the accuracy of battery exterior defect inspection. In actual battery production lines, the occurrence rates of defects vary by defect type, making it challenging to create a large, uniform defect dataset due to the time required for defect acquisition. This leads to a reduction in the accuracy of battery exterior defect inspection. To construct a large, uniform defect dataset, this paper proposes a defect-background separated GAN based on the principles of GANs. The defect-background separated GAN performs effective defect and background separation learning by referring to the segmentation labeling of defects. Through dataset augmentation using the defect-background separated GAN, the performance quality of newly generated synthetic defect images has improved, and accuracy in battery exterior defect inspection can be enhanced through extensive dataset training. Experimental results show the lowest Fréchet inception distance score among various other methods for the battery exterior defect dataset, and it generates clear synthetic defects perceptible to the human eye. By training defect segmentation on this large, uniform defect dataset, an accuracy of 96.1% and an intersection over union value of 0.71 were achieved. Ultimately, applying this defect inspection network to the actual production line demonstrated a 72% improvement in time efficiency. This demonstrates the stability and robustness of the large, uniform defect dataset generated through the defect-background separated GAN.

It is important in production to achieve accurate counting and density estimation of high-density culture fry under the environmental conditions of aquaculture scenarios in an efficient and accurate manner. However, none of the current methods for fry counting works well under the high-density and high-overlap conditions of real aquaculture scenarios. Therefore, in this paper, we propose a high-density farming fry monitoring network model, Super-Resolution GAN Density Estimate Attention Network (SGDAN), which incorporating an image enhancement algorithm and an attention mechanism, and we create a high-density farming fry dataset (HD-FryDataset) based on the environmental conditions of real aquaculture scenarios. The network model is designed to improve and optimize the targeted subnetworks for several key aspects of high-density fish fry monitoring work. Four subnetworks are included for image optimization, feature extraction, attention, and density map estimation. The experimental results show that the SGDAN network model achieved an average counting accuracy of 97.57% on the high-density culture fry dataset, which was 8.23% and 2.06% higher than those of MCNN and CSRNet, respectively. Additionally, the MAE and RMSE of the model were reduced by 71.9% and 67.3% and by 34.3% and 33.2% compared with those of MCNN and CSRNet, respectively. The model proposed in this paper also has a better ability to generate predictive density maps. The density maps generated by SGDAN have values of the evaluation metrics PSNR and SSIM of 20.33 and 0.933, respectively, which are 3.31 and 0.037 and 2.63 and 0.031 higher than those of MCNN and CSRNet. In general, the network model proposed in this paper outperforms existing network models in two applications: accurate counting of fry and generation of density maps for high-density culture in aquaculture. It also provides a good solution for digitizing the number of fry and visualizing the density of high-density culture in intelligent aquaculture systems.

Vision-based smart agriculture is an important way to improve the efficiency of agricultural production. Labeling images for deep learning in complex field photos is a difficult task. In this paper, an image augmentation method that can help reduce the workload of image labeling based on synthetic images is proposed. The synthetic images consisted of three parts: crop, weed, and soil. The crop and weeds were obtained automatically by Excess Green (ExG) and minimum error threshold segmentation. The data augmentation method was tested on image classification, object detection, and semantic segmentation tasks by Resnet, YOLOV5, and DeeplabV3. The accuracy of the classification model reached 0.99. The IoU of object detection and semantic segmentation were 0.98 and 0.96, respectively. The results showed that the method in this paper was acceptable despite slight overfitting. This method was proposed based on the characteristics of field images, and it was meant for reducing the workload of labeling images.

The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of text generators. With the potential for misuse escalating, the importance of discerning whether texts are human-authored or generated by LLMs has become paramount. Several preceding studies have ventured to address this challenge by employing binary classifiers to differentiate between human-written and LLM-generated text. Nevertheless, the reliability of these classifiers has been subject to question. Given that consequential decisions may hinge on the outcome of such classification, it is imperative that text source detection is of high caliber. In light of this, the present paper introduces DeepTextMark, a deep learning-driven text watermarking methodology devised for text source identification. By leveraging Word2Vec and Sentence Encoding for watermark insertion, alongside a transformer-based classifier for watermark detection, DeepTextMark epitomizes a blend of blindness, robustness, imperceptibility, and reliability. As elaborated within the paper, these attributes are crucial for universal text source detection, with a particular emphasis in this paper on text produced by LLMs. DeepTextMark offers a viable “add-on” solution to prevailing text generation frameworks, requiring no direct access or alterations to the underlying text generation mechanism. Experimental evaluations underscore the high imperceptibility, elevated detection accuracy, augmented robustness, reliability, and swift execution of DeepTextMark.