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Lightweight Vit-Based Pedestrian Multi-Object Detection in Building Construction Scenarios

Lightweight Vit-Based Pedestrian Multi-Object Detection in B...

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Machine vision, image processing and Imaging Technology (MVIPIT), international conference on

作者： Jiayu Dai Jinshan Pan Fenglei Xu Zhongwei Shen Department of Electronics and Information Engineering Suzhou University of Science and Technology Suzhou China

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

In the construction environment, significant safety hazards and pressing safety detection requirements are evident. Multi-target detection technology plays a pivotal role in ensuring real-time monitoring of safety and regulatory compliance at construction sites. Nevertheless, the intricate nature of the construction environment, characterized by cluttered backgrounds, diverse personnel, and challenging equipment deployment conditions, poses formidable challenges to the efficacy of multi-target detection algorithms. To address these obstacles, we propose a detection methodology that integrates lightweight vision Transformer (ViT) and lightweight Feature Pyramid Network (FPN). The Lite vision Transformer (LVT) is a streamlined backbone network that effectively reduces network parameters while maintaining high detection accuracy by optimizing convolutional self-attention and recursive atrous self-attention mechanisms. Additionally, the Feature Pyramid Network (FPN) utilizes spatial multi-scale feature maps to enhance target detection precision. This approach excels in multi-scale target detection and identifying obscured objects, making it suitable for deployment in resource-constrained construction sites. Experimental assessments conducted on the COCO dataset reveal that our method reduces parameter count by approximately 60% compared to the YoloV3-Tiny model while improving accuracy by roughly 5% over traditional lightweight CNN networks.

关键词： computer vision Accuracy Computational modeling Buildings Object detection Feature extraction Transformers Robustness Real-time systems Convolutional neural networks

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Dual-diagnostic Method for Depression Patients Based on BERT Model and ViT Model for Audio and Text Analysis

Dual-diagnostic Method for Depression Patients Based on BERT...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Liuqing Wang Qingning Zhang Shanghai New Epoch Bilingual School Shanghai China SINO-Canada SCHOOL Suzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In the face of escalating global mental health challenges, the rapid and accurate diagnosis of depression has become more crucial than ever. Depression, a pervasive mental health disorder, often goes undiagnosed due to its complex symptomatology and the limitations inherent in traditional diagnostic methods. Current approaches, relying heavily on patient self-reporting and clinical observation, fall short in capturing the multifaceted nature of depression, leading to potential delays in treatment and, consequently, exacerbated health outcomes. Addressing these shortcomings, this paper introduces a novel dual-diagnostic approach leveraging the advanced capabilities of AI, specifically through the integration of BERT and vision Transformer (ViT) models. Our methodology utilizes BERT for the analysis of conversational text data and ViT for the examination of audio spectrograms from patients with depression, offering a comprehensive and nuanced understanding of depressive states. This dual-modality approach not only addresses the limitations of existing single-mode diagnostic practices but also significantly enhances the accuracy and efficiency of depression diagnosis. By harnessing the strengths of both textual and auditory data analysis, our proposed method stands as a testament to the potential of AI in revolutionizing mental health diagnostics, paving the way for timely interventions and personalized care strategies that are critically needed in the medical and clinical domain.

关键词： Analytical models computer vision Accuracy Mental health Depression Transformers Artificial intelligence Spectrogram Medical diagnostic imaging Context modeling

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Research on Expression Recognition Algorithm Based on University Students' Mental State

Research on Expression Recognition Algorithm Based on Univer...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Guangli Luo Guangzhou Institute of Science and Technology Guangzhou City China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

With the continuous development of higher education in our country, the number of college students is increasing year by year. There are more and more campus accidents caused by college students' psychological problems, which has aroused people's great attention to college students' psychological problems. Many scholars have conducted in-depth research on college students' psychological problems. To solve students' psychological problems, we must first find out the problems in time. With the improvement of the level of social information, face recognition technology integrates multiple key technologies such as computer vision, artificial intelligence, and deep learning, which has been widely used in different fields of society. However, there are few researches and applications on the mental health status of college students. Therefore, this paper combines the expression recognition technology with the psychological problems of college students, fully excavates the static and dynamic features of dynamic expression behavior through convolutional neural networks, establishes a facial expression recognition model, and conducts comparative experiments through open data sets to verify the accuracy of different algorithms, so as to timely discover the psychological problems of college students. The method of expression recognition is used to control the psychology of college students.

关键词： Emotion recognition Accuracy image recognition Face recognition Heuristic algorithms Mental health Feature extraction Stability analysis Convolutional neural networks Accidents

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A comparative analysis on the existing techniques of wheat spike detection 2

A comparative analysis on the existing techniques of wheat s...

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2nd international conference for Emerging Technology, INCET 2021

作者： Thakur, Anupam Kumar Singh, Suraj Goyal, Neha Gupta, Kapil National Institute of Technology Department of Computer Applications Haryana Kurukshetra India

ISBN: (纸本)9781728170299

Wheat is one of the essential crops for humans. A fair amount of work is done related to wheat, such as determining the phonological stages of wheat plants, the reckoning number of wheat spikes, and detecting diseases like Fusarium head blight(FHB) in wheat plants, measuring the quality of wheat grains. Wheat head detection is a primary task among all the tasks. Object detection is nowadays affecting researchers, and several approaches are related to machine learning, image processing, computer vision, and deep learning are discussed in the literature. This paper provides a comparative analysis of variegated object detection methods used in different applications on wheat head detection and various challenges for wheat detection. With comparative analysis, we are comparing mostly used CNN-based object detection models. © 2021 IEEE.

关键词： Object detection

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Lightweight image Deraining Network Based on Dilated Depthwise Separable Convolution and Enhanced Channel Attention

Lightweight Image Deraining Network Based on Dilated Depthwi...

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Signal processing and Intelligent Computing (SPIC), international conference on

作者： Sen Lin Guang Zhou Yonghua Tang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Liaoning Key Laboratory of Machine Vision Shenyang University of Technology Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Rainy conditions can introduce significant challenges for various computer vision tasks such as detection and recognition. Addressing the limitations of existing image deraining models in terms of model complexity and performance, this paper proposes a lightweight and efficient deraining network. Firstly, it proposes a multi-scale image feature extraction method based on a shifting window and a fusion method for image features, enhancing the representation of rain streaks. Then, it constructs a separable feature channel attention mechanism to extract rain streak features meticulously, accurately, and efficiently. Finally, it designs a collaborative expansion, depth separable, and globally enhanced detail restoration network to compensate for the loss of some rain streak details that the network may experience. Compared with the traditional image training network, the network significantly reduces the number of parameters, thereby greatly shortening the training time. In addition, the network also showed excellent performance in rainy conditions. Experimental results show that the proposed algorithm performs well on public datasets such as Rain200L, Rain200H and Rain800, and can effectively remove rain artifacts from images.

关键词： Training image quality Rain Convolution Computational modeling Collaboration Feature extraction Complexity theory image restoration Optimization

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Comprehensive Review for Video Surveillance Based Suspicious Human Activity Detection

Comprehensive Review for Video Surveillance Based Suspicious...

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Advances in Computation, Communication and Information Technology (ICAICCIT), international conference on

作者： Lucky Rajpoot Rosy Madaan School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The detection of potentially illicit behaviors from recorded video footage is an emerging field of study in the domain of image processing and computer vision. Detecting suspicious activities is essential for maintaining the security of both businesses and communities. Surveillance cameras are primarily utilized in public areas to monitor and guarantee security. To provide continuous monitoring of public spaces, it is crucial to utilize intelligent video surveillance systems capable of properly detecting and classifying human actions in real-time as either benign or potentially threatening. This study presents a state-of-the-art review that highlights the general progress made in the last several years in spotting suspicious behaviors from surveillance recordings. article briefly summarize the problems and challenges related to identifying questionable human behaviors. This article aims to provide researchers in this subject with an overview of the literature on a number of suspicious activity recognition systems, including an overview of their general architecture.

关键词： Reviews image processing Video surveillance Real-time systems Behavioral sciences Safety Recording Information and communication technology Security Object recognition

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computer vision Based Analysis for Fused Filament Fabrication Using a G-Code Visualization Comparison 2nd

Computer Vision Based Analysis for Fused Filament Fabricatio...

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2nd international conference on Cyber-Physical Systems and Control, CPS and C 2021

作者： Schindler, Fabian Aburaia, Mohamed Katalinic, Branko Lackner, Maximilian Stuja, Kemajl UAS Technikum Vienna Höchstädtplatz 6 Vienna1200 Austria TU Wien Karlsplatz 13 Vienna1040 Austria

ISBN: (纸本)9783031208744

The first open-source and low-cost fused filament fabrication 3D printer of 2004 represented a new opportunity to manufacture mechanical components easily and affordably. 3D printers have been continuously developed since the first one entered the market and are equipped with various sensors that can monitor the printing process. Despite these improvements, a probability of 41.1% remains that the printed part will have errors. This can lead to an irreparable print that has to be canceled, but is often not noticed by common sensors, while costly time, electricity and filament continue to be consumed. This investigation provides an account of a camera based monitoring system developed to detect complex problems that are not easily recognizable with sensors commonly used for fused filament fabrication. image segmentation was used to remove the background of the printed part and the result was compared to a visualization of the G-Code. By using an exclusive-or method it was possible to determine differences, which can indicate defects. Depending on the similarity, the printing process can be canceled promptly. Tests have demonstrated that this method works reliably even under changing lighting conditions in most cases but can lead to poor segmentation due to shadows being cast in the infill. The application is also able to recognize differences when printed parts detach or layers have shifted if they are not covered by lower layers. The use of a light source on top of the 3D printer and additional cameras, beside the build plate, could solve both problems in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Additive manufacturing Fused filament fabrication image processing Process monitoring

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A Deep Learning-Based Bengali Visual Commonsense Reasoning System

A Deep Learning-Based Bengali Visual Commonsense Reasoning S...

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computer Graphics and image processing (CGIP), international conference on

作者： Md Yousuf Zaman Md Tareq Zaman Sounava Pandit Jeba Sultana Faisal Muhammad Shah Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350374186

ISBN: (纸本)9798350374193

Visual commonsense reasoning, an integral aspect of human intelligence, extends beyond mere object identification, encompassing the nuanced inference of actions, intentions, and emotions from visual content. While humans effortlessly excel in this cognitive domain, contemporary Bengali vision systems encounter obstacles in attaining comparable proficiency. This is primarily attributed to the need for advanced cognitive faculties and common-sense reasoning abilities. This research paper introduces the Bengali Visual Commonsense Reasoning System (BVCRS), differentiating from prevailing Bengali vision models that predominantly focus on tasks such as image captioning, object recognition, segmentation, and visual question answering. The Bengali Visual Commonsense Reasoning(BVCR) model, coupled with our Bangla-VCR dataset, stands as a pivotal asset for advancing research in Bengali visual understanding and reasoning. Our BVCR model aspires to capture real-world dynamics and human intentions in Bengali by incor-porating state-of-the-art deep learning *** leverage a pre-trained ResNet50 for visual features and fine-tuned M-BERT for Bengali *** extract relationships between textual and visual features for deducing answers and reasoning, a bidirectional LSTM with attention mechanism is employed. In experiments, BVCR achieved 42.6% accuracy in visual question answering and 44.2% in visual commonsense reasoning. Our work highlights the challenges in attaining human-level performance in Bengali visual reasoning, providing insights for future research.

关键词： Deep learning Visualization Analytical models Human intelligence Computational modeling Feature extraction Question answering (information retrieval)

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Analysis of simultaneous localization and mapping technology for mobile robot based on binocular vision

Analysis of simultaneous localization and mapping technology...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Ziheng Xue Faculty of Engineering University of Bristol University of Bristol Beacon House Bristol UK

With the increasing demand for intelligent robot applications over the years, SLAM technology is also developing rapidly as an essential part of robot perception modality. SLAM is a vast system that involves multi-domain knowledge such as image pre-processing, matching, and camera pose estimation. In this paper, the basic architecture of SLAM based on binocular vision is elaborated, and some applications of its methods are presented in conjunction with new methods in recent years. The primary approach is to separate the front-end and back-end and achieve good tracking and map-building results using a multi-threaded approach. The front-end uses the SHI-Tomasi algorithm in OpenCV to detect the feature points, triangulation to calculate the landmark depth, and Lucas-Kanade optical flow method for tracking from one frame to the next and computing the pose transformation. G2O is used in the SLAM back-end to optimize the poses and nonlinearities. The analysis of SLAM based on binocular vision also provides an essential reference for people new to visual SLAM.

关键词： Electrical engineering Visualization Simultaneous localization and mapping Robot vision systems Pose estimation computer architecture Feature extraction

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A Review of the Evolution and Applications of Convolutional Neural Network (CNN)

A Review of the Evolution and Applications of Convolutional ...

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international conference on Edge Computing and Applications (ICECAA)

作者： Sandeep Joshi Mehul Manu Amit Mittal Department of Computer Science and Engineering Graphic Era Hill University Bhimtal India Department of Allied Sciences Graphic Era Hill University Bhimtal India

Within this literary document, a panoramic insight is presented on the progression and practical uses of Convolutional Neural Networks (CNNs), an influential technique in deep learning that serves as a major element within computer vision research alongside other areas. Through a comprehensive analysis of the literature, this research study presents thehistorical development of CNNs from early work on perceptrons to current state-of-the-art architectures like VGGNet, ResNet, and EfficientNet. The review highlights the key contributions of CNNs in various fields, such as image and video recognition, natural language processing, and audio analysis. Furthermore, it discusses the potential for further research and development of CNNs, including the challenges in training and optimizing CNNs and the future directions of CNNs. Overall, this review underscores the importance of CNNs in enabling breakthroughsin diverse fields and their potential for continued impact on the scientific community.

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