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vision Calibration of Complex Micro-Device Flexible Assembly Robot System 3

Vision Calibration of Complex Micro-Device Flexible Assembly...

引用

3rd international conference on control and Intelligent Robotics, ICCIR 2023

作者： Yiwei, Hu Wenrong, Wu Yi, Yang China Academy of Engineering Physics Laser Fusion Research Center Mianyang621900 China

ISBN: (纸本)9781510671867

Aiming at the complex micro-device assembly robot system that has been built, based on the research on the composition of the motion mechanism and the micro-vision system, and the research on the micro-vision imaging system, derived image Jacobian matrix for parts assembly control based on single-channel or multi-channel micro vision, and obtained the conversion relationship between the pose change of the motion mechanism coordinate system and the micro-vision. And through the method of active movement of each movement mechanism, the image Jacobian matrix calibration between the macro-moving robot, the parts adjustment table, the micro-moving module and the micro-vision is realized. A part detection and adjustment strategy are formulated to realize rapid and effective control of micro parts. Experimental results verify the correctness and accuracy of the calibration method. © 2023 SPIE.

关键词： Calibration

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Application of deep learning in pothole road recognition: a YOLOv5 method combining ASFF and SimAm 3

Application of deep learning in pothole road recognition: a ...

引用

3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Lu, Weiquan Zhang, Jiarong Ju, Jinli Liu, Ziyu Zhao, Yuxiang Qin, Rong Lu, Danni Zhou, Xiaolu School of Information Engineering Xi'an Mingde Institute of Technology Xi'an710124 China School of Intelligent Manufacturing and Control Technology Xi'an Mingde Institute of Technology Xi'an710124 China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan430200 China

ISBN: (纸本)9781510680449

In the application of computer vision recognition, the recognition objects of some tissue-like structures present highly complex and variable characteristics. For this practical usage scenario, traditional classification algorithms usually cannot achieve the desired recognition effect. To achieve better recognition performance in this direction, this study integrates ASFF into the YOLOv5 model and introduces the SimAm attention mechanism module. The improved network structure is more lightweight, has fewer network parameters, and the attention mechanism is more effective. We trained the modified YOLO model on datasets, and experimental results show a significant increase in mAP value and a 20% increase in accuracy. This indicates that improvements to the model can significantly enhance performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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A Design Method of Aircraft Simulation System 3

A Design Method of Aircraft Simulation System

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Sun, Yong Zhou, Qiang Cao, Junqing Zhang, Jingxiao Geng, Hongyue Wei, Lan China Research and Development Academy of Machinery Equipment Beijing China

ISBN: (纸本)9781665459112

This paper presents a design method of aircraft simulation system. Based on this method, the relevant software is realized for the simulation of aircraft attacking ground targets. This method is composed of aircraft maneuver process simulation module, target reconnaissance and intelligence processing process simulation module, target positioning simulation module, air to ground missile simulation module, and so on. © 2022 IEEE.

关键词： computer software

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Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and computer vision Systems 3

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

引用

3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Adewopo, Victor Elsayed, Nelly Elsayed, Zag Ozer, Murat Zekios, Constantinos L. Abdelgawad, Ahmed Bayoumi, Magdy Univ Cincinnati Sch IT Cincinnati OH 45221 USA Florida Int Univ Dept Elect & Comp Engn Miami FL USA Cent Michigan Univ Sch Engn & Tech Mt Pleasant MI USA Univ Louisiana Dept Elect & Comp Engn Lafayette LA USA

ISBN: (纸本)9798350372977;9798350372984

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： Traffic Surveillance Accident Detection Action Recognition Smart City Autonomous Transportation

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computer vision Based Algorithms for Detecting and Classification of Activities for Fall Recognition on Real Time Video 3

Computer Vision Based Algorithms for Detecting and Classific...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kandukuru, Tirumala Kumar Thangavel, Senthil Kumar Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

Falls among the elderly population present significant risks, including serious injuries and even death. Prompt detection of falls is crucial for immediate assistance and notification to relevant parties. However, current fall detection systems face challenges and privacy concerns. To address this, a new system is proposed using a Convolutional Neural Network (CNN) and optimizing Optical Flow images through preprocessing in spatial and frequency domains. The system incorporates the Viola-Jones algorithm for human face and action recognition. It is designed for detecting falls in home or room environments, covering various postures - standing, sitting, walking, and falling. Surveillance cameras in public spaces capture relevant data for training the model. The system achieves impressive accuracy and precision, highlighting its effectiveness in fall detection applications. © 2024 IEEE.

关键词： Fall detection

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Research on target measurement based on fusion of binocular and TOF system 3

Research on target measurement based on fusion of binocular ...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Xia, Xinghua Xu, Zilong Han, Zhonghua Chen, Zhili Shenyang Jianzhu University School of Electrical and Control Engineering Shenyang110016 China

ISBN: (纸本)9781665459112

Traditional target detection mostly uses manual detection, which has poor universality and takes a long time. It is difficult to measure some large-scale three-dimensional objects. Therefore, the use of three-dimensional vision for object detection has become a hot spot. According to the complementary advantages of binocular system and TOF system, the two systems are combined effectively. The coordinate relation between TOF system and binocular system is established by joint calibration. The data obtained by the TOF camera were mapped to the camera's perspective on the left side of the binocular system to obtain the TOF parallax map under the camera's perspective on the left side of the binocular system. The parallax map was transformed into the resolution of binocular image through image processing. The concept of confidence degree is put forward. According to the confidence degree, the different system weights of data fusion are determined. After fusion, the matching accuracy is improved, and fast 3D reconstruction data acquisition can be completed. © 2022 IEEE.

关键词： Stereo image processing

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A novel blind action quality assessment based on Multi-headed GRU network and attention mechanism 3

A novel blind action quality assessment based on Multi-heade...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Sun, WenHao Hu, YanXiang Zhang, Bo Chen, XinRan Hao, CaiXia Gao, YaRu College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Objective action quality assessment (AQA) is a complex machine vision task because existing AQA assessment models can’t effectively fit the subjective assessment. To address this issue, we propose a novel blind action quality assessment method. By processing the video data with spatial and temporal features, the performance of the model is effectively improved. In addition, we also proposed a new loss function to better train the model, which combines the information entropy of the data. Finally, the experimental results show that on the existing datasets AQA-7 and JIGSAWS are significantly improved, reaching 0.63 and 0.57, respectively. © 2023 SPIE.

关键词： Recurrent neural networks

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Research and design of machine vision-based defect detection method for 3C digital screens with Multi-feature fusion 3

Research and design of machine vision-based defect detection...

引用

3rd international conference on Image Processing and Media Computing (ICIPMC)

作者： Zhao Jianjun Dong Xiang Zhu Zhenggen Zheng Zhong Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386660;9798350386677

This paper aims at the current digital screen defect detection in the field of the feature region is not clear, a single screen samples on the screen and the line part of the complexity of the problem of simultaneous detection. It proposes an inspection system design method that realizes digital screen defect detection and classification by superimposing the sample bright field and dark field images, complementing traditional machine vision and computer vision solutions, and adopting a multi-feature fusion method to detect the rows of wires that cannot be detected due to overexposure in the bright field by using dark field images;and detecting the main part of the screen with lower contrast in the dark field by using bright field images. The algorithm adopts the improved YOLOv5 deep learning model, and the feature extraction network selects the lightweight GhostNet, and the feature information extracted from the screen pictures is fused, defect recognition and classification through the bidirectional multi-scale fusion network. Comparison with the manual detection results shows that: the detection rate accuracy of the combined bright and dark field defect detection device using the improved YOLOv5 algorithm is 6.8% higher than that of the original YOLOv5 algorithm device;and the detection time of a single piece of defect detection is reduced by 67.4% compared with that of the manual defect detection. The experimental results prove that the defect detection device has high value and research significance.

关键词： Multi-feature YOLOv5 Defect Detection

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening 3

Bio-inspired Heuristic Optimization-based Cascaded Network f...

引用

3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Shuvo, Eshat Ahmad Rahman, Wahidur Hossain, Md Shakhawat Islam, Md. Tarequl Iqbal, Md. Sadiq Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Center Independent University Dept. of Computer Science & Engineering & RIoT Bangladesh Khwaja Yunus Ali University Dept. of Computer Science & Engineering Sirajganj Bangladesh

ISBN: (纸本)9798350388282

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features;the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.

关键词： Large datasets

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Noise characteristics analysis and control technique for profile-shaped buildings considering structure-acoustic sensitivity 3

Noise characteristics analysis and control technique for pro...

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3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Du, Danyou Xiamen Institute of Technology Xiamen361021 China

ISBN: (纸本)9781510680449

Because of the complex geometry and changeable materials, it is difficult to control the noise in the interior of special-shaped buildings. For this reason, a new method is proposed to analyze and control the indoor noise characteristics of special-shaped buildings considering the structure-acoustic sensitivity. This paper analyzes the indoor noise characteristics of special-shaped buildings, calculates the indoor structural noise of special-shaped buildings, analyzes the structure-acoustic sensitivity, and obtains the normal velocity amplitude sensitivity of structural response by using finite element method. Sound waves are soundproofed by using the different vibration characteristics of materials to control the indoor noise of special-shaped buildings. The experimental results show that the acoustic sensitivity of the proposed method is higher than 70dB, the longest control time is 29.88s, the peak value of indoor vibration basically disappears, and the abnormal noise completely disappears. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Buildings

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