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In order to realize the digital management and manufacturing of air material, the methods of 3D point cloud enhancement optimization and high-fidelity texture reconstruction in entity digitization of air material based on the fusion of 3D scanning and neural rendering are proposed. In this paper, an automatic data acquisition system is designed and built, and the acquired training images are enhanced. Then the Yolov8 segmentation model and CascadePSP boundary refinement model are combined for training to realize foreground segmentation and background removal of air material images. In this paper, a 3D point cloud enhancement optimization method is proposed, which combines the point cloud obtained by binocular structured light scanner and the point cloud reconstructed by Colmap algorithm for registration and fusion. By combining the coarse registration of PointNetLK network with the fine registration of ICP algorithm, a more complete and high-quality dense point cloud is generated, and the effects of illumination conditions and image quantity on point cloud reconstruction is studied experimentally. Using the optimized point cloud model and random perspective enhancement (RPE) method, the neural rendering model NeuS is improved, which improves the 3D surface texture reconstruction quality and the sight extrapolation effect. In order to verify the effectiveness of the proposed method, comparison and ablation experiments are carried out in this paper. The experimental results show that point cloud enhancement optimization and RPE have a good effect on improving the model, and the model performance has been greatly improved. [ABSTRACT FROM AUTHOR]

Transformers are Deep Neural Networks (DNN) that utilize a self-attention mechanism to capture contextual relationships within sequential data. Unlike traditional neural networks and variants of Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), Transformer models excel at managing long dependencies among input sequence elements and facilitate parallel processing. Consequently, Transformer-based models have garnered significant attention from researchers in the field of artificial intelligence. This is due to their tremendous potential and impressive accomplishments, which extend beyond Natural Language Processing (NLP) tasks to encompass various domains, including Computer Vision (CV), audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published, spotlighting the Transformer's contributions in specific fields, architectural disparities, or performance assessments, there remains a notable absence of a comprehensive survey paper that encompasses its major applications across diverse domains. Therefore, this paper addresses this gap by conducting an extensive survey of proposed Transformer models spanning from 2017 to 2022. Our survey encompasses the identification of the top five application domains for Transformer-based models, namely: NLP, CV, multi-modality, audio and speech processing, and signal processing. We analyze the influence of highly impactful Transformer-based models within these domains and subsequently categorize them according to their respective tasks, employing a novel taxonomy. Our primary objective is to illuminate the existing potential and future prospects of Transformers for researchers who are passionate about this area, thereby contributing to a more comprehensive understanding of this groundbreaking technology. • The paper presents a comprehensive survey on transformers for deep learning tasks. • The paper conducts a thorough analysis on highly effective models in five domains. • The paper classifies the models based on respective tasks using a proposed taxonomy. • The characteristics of the surveyed models are deeply explored and analyzed. • Future directions and challenges for transformer-based models are deciphered. [ABSTRACT FROM AUTHOR]

A deep learning-based intelligent system has been developed for the identification and detection of high-density small target pests with the aim of addressing the limitations observed in previous detection systems and manual sorting. The novel system promises to overcome issues of low detection accuracy, low detection efficiency, and serious leakage and error detection. It is expected to significantly improve the efficiency of pest detection, thereby offering a potential solution to the challenges posed by the presence of these pests in agricultural settings. In this paper, we propose an enhanced YOLOv5s model to tackle challenges in pest detection, such as small targets, high densities, and species diversity. Our method embeds Channel Attention (CA) modules into the algorithm and broadens the shallow feature detection scale of the original FPNet, bolstering small-target detection. Network convergence is accelerated using the Kmeans++ algorithm for prior frame generation and YOLOv5s' intrinsic weights for transfer learning. The Efficient Intersection over Union (EIOU) loss function is adopted to address unbalanced data labels. Furthermore, we develop an algorithm to determine pest infestation levels, transforming detection results into pest grade evaluations. These advancements form the core of our proposed intelligent pest identification and detection system. In our enhanced YOLOv5s model, we assessed the system's performance using a dataset comprising six prevalent pest species. This dataset was specifically curated to emulate real-life detection scenarios, featuring small pest targets with similar appearances and imbalanced data labels. We compared our model with a common one-stage model and noted average improvements of 5.5%, 2%, and 3.95% in accuracy, recall, and detection precision respectively across both sparse and dense scenarios. Importantly, the enhanced model maintained a detection time of roughly 10ms per image and was only 1MB larger than the original model, illustrating a balance between accuracy and efficiency. Furthermore, our algorithm enables the assessment of pest grades based on the input detection results, illustrating the degree of pest infestation. The present paper describes the development of an intelligent system for the automatic identification and detection of pests. Experimental results demonstrate the high efficiency and efficacy of the proposed system in identifying and detecting common pests, even in complex scenarios characterized by high pest density and data label imbalance. Notably, the proposed system has the potential to assist practitioners in the fields of agriculture and forestry to make informed decisions on pest control, thereby improving work efficiency and productivity. • An intelligent system for pest identification development was developed. • An improved YOLOv5 model is proposed. • An algorithm was developed to determine the level of pest infestation. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy. • A survey on the current state of imaging spectroscopy. • Pre-processing techniques and data acquisition methods are highlighted. • We provide applications of imaging spectroscopy in various domains. • Publicly available databases and software tools for spectral data are presented. [ABSTRACT FROM AUTHOR]

The detection of underwater fish targets is critical for ecological monitoring and marine biodiversity research. However, underwater fish detection is typically constrained by problems including low image quality and variable underwater surroundings. On behalf of further improving the underwater fish detection accuracy in complex underwater environments, this paper proposes a dual-path (DP) Pyramid Vision Transformer (PVT) feature extraction network named DP-FishNet. The backbone network DP-PVT composed from the PVT Network is made up of two feature extraction paths. The first represents the Vision Transformer path, which extracts global features to enhance the distinction between the foreground and background of underwater images. The second is the convolutional neural network path, which enhances the accuracy of detecting small targets by extracting local features. Additionally, to more effectively utilize the feature information extracted by the network, this paper provides a promising solution to employ the content-aware reassembly of features (Carafe) in the feature pyramid network (FPN). The seesaw loss is utilized as a classification loss to address the problem of unbalanced samples caused by the gap in the number of fish populations. According to the experimental findings, the AP and AP50 of the DP-FishNet are 76.0% and 95.2%, respectively. In comparison to currently available advanced two-stage detection algorithms, the quantity of computation and parameters is reduced by approximately 40%. DP-FishNet strengthens the ability to extract global and local features from underwater images and enhances feature reuse. DP-FishNet can be utilized to detect fish targets in actual and complicated underwater habitats. [ABSTRACT FROM AUTHOR]

In recent years, natural language processing (NLP) has gained new territory beyond its traditional use in text mining applications. This paper shows the effectiveness of NLP techniques in assessing the apparent human personality from his/her video transcript, building a bridge between NLP and computer vision-based reasoning. In this paper, a new deep learning model using attention mechanism and bidirectional LSTM layers for estimating the Big-five personality traits is provided and then tested on ChatLearn video dataset. The robustness of the approach is then tested by generalizing the method to two other datasets (B5 corpus and Mypersonality datasets). Several empirical evaluations taking into account the various inputs of the data processing pipeline have been performed to yield optimal model parameters. The developed model is tested on the APA'2016 competition dataset from Chalearn V2 Challenge Workshop for both Big-five personality trait estimation and interview score estimation. We achieved an average of 89.27% in personality trait recognition rate and 89.16% score in the Job Interview challenge. Similar trends of high accuracy score estimation is held for B5-corpus and MyPersonality datasets as well. The results outperformed several state-of-art approaches, demonstrating our approach's feasibility in extending the computer vision approach of first impression personality to natural language processing. This opens up a new direction in multimedia analysis. • New big five personality trait estimation using deep learning and NLP was developed. • The approach was tested successfully on ChatLearn video dataset. • Generalization is demonstrated using B5 corpus and MyPersonality dataset. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets. • An AV behaviour prediction system must have a long (> 3s) prediction horizon. • It is a challenging task to model interaction between agents. • Datasets from onboard sensors should be used to predict road agents' behaviour. • Deep learning techniques have shown better results to predict behaviours. • Algorithms should be evaluated using the same datasets and evaluation metrics. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually evolving into modern Chinese characters. However, because of the intrinsic limitations of traditional convolutional networks in the model, the geometric structure of the convolutional module is fixed. Therefore, we build a new architecture called attention-based deformable networks for this task. Besides, we construct a new dataset called the Chinese characters from Various Dynasties Dataset (CCDD), which includes the evolution of Chinese characters between major dynasties. Deformable convolution based on attention mechanism can obtain those more important offsets, resulting in better performance than the original deformable convolution. In the experimental stage, extensive experiments validate the performance of our approach. Firstly, the effectiveness of this network was verified on two text datasets, Mnist(99.38%) and notMnist(98.22%) both comparing with several modules. Finally, the best performance was achieved on the CCDD dataset(91.50%) compared to other classical deep learning networks. This is the first paper to apply deep learning methods to recognize and classify the characters of major dynasties in China. And archaeological workers need to be able to recognize Chinese characters from various periods in China, and our algorithm can assist them in completing this task to a certain extent. It can also help cultural relic enthusiasts roughly determine the age of cultural relics. [ABSTRACT FROM AUTHOR]