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检索条件"主题词=Detection and tracking algorithms"

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A Student behavior Recognition Algorithm Based on Improved MobileNetV2 8

A Student behavior Recognition Algorithm Based on Improved M...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Zhou, Zheng Hirota, Kaoru Dai, Yaping Mersha, Bemnet Wondimagegnehu Dai, Wei Lin, Yumin Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China River Security 1520 Gumei Road Xuhui District Shanghai China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Student behavior recognition is crucial for detecting learning activities within the classroom. Addressing the high complexity, poor real-time performance, and low accuracy of existing recognition models, we develop a student behavior classification dataset and propose an algorithm for student behavior recognition based on improved MobileNetV2. The algorithm diminishes model complexity and accelerates inference speed by reducing the width factor and improves model accuracy by improving classifier of the model and embedding coordinate attention block. The ablation experiment demonstrates the algorithm’s superior performance, achieving an average accuracy of 92.45% on the dataset, a 2.64% improvement over the initial model. The parameter is also significantly reduced to 0.56M, marking a 75% decrease. Computational requirements are lowered to 1.67 GFLOPs, representing a 68% reduction. Furthermore, the inference speed is enhanced to 244 FPS, a 106.7% increase compared to the initial model. Comparative experiment indicates that our algorithm outperforms other lightweight neural networks, including EfficientNet, DenseNet, ShuffleNetV2, and MobileNetV3, in terms of model complexity, inference speed, and accuracy. © 2025 SPIE.

关键词： Convolution Data modeling Education and training detection and tracking algorithms Feature extraction Performance modeling Cell phones Instrument modeling Mobile devices Neural networks

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Research on Lightweight Algorithm for Cargo Packaging Unsealing detection Based on Improved YOLOv5 8

Research on Lightweight Algorithm for Cargo Packaging Unseal...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Luo, Longyu Wang, Yuexin Yang, Xiaolong Lin, Wei Huang, Liang Fujian University of Technology China Longyan University China Longhe Intelligent Equipment Manufacturing Co. Ltd. China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

In order to realize the detection problem of whether the goods package has been opened by intelligent forklift in warehousing and logistics, an object detection algorithm SSA-YOLOv5n based on improved YOLOv5n is proposed in this paper. Replacing the backbone network with ShuffleNetV2 reduces the number of parameters, calculation and weight file size of the detection model, and improves the detection speed, which is convenient for intelligent forklift trucks mobile deployment. The non-parametric attention mechanism SimAM is introduced at the end of the backbone network to improve the recognition accuracy significantly without increasing the parameters. The CIoU loss function is replaced by IoU loss function, which improves the detection accuracy of the model. The experimental results show that compared with the original model YOLOv5n, the accuracy of the algorithm is improved by 12.9%, the number of parameters is reduced by 1.54M, the calculation amount of the model is reduced by 3.6B, and the weight file is reduced from 3.64MB to 0.67MB, which has a good performance for the detection about whether the cargo package is open or not. © 2025 SPIE.

关键词： detection and tracking algorithms Object detection Packaging Machine vision

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Research on road object detection algorithms based on YOLOv8-RC 4

Research on road object detection algorithms based on YOLOv8...

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2024 4th International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Li, Wenqiang Sheng, Hu Geng, Na Yang, Xili Xu, Chuanxin School of Computer and Communication Engineering Dalian Jiaotong University Liaoning Province Dalian116028 China

ISBN: (纸本)9781510686304

In response to issues of low detection accuracy and poor robustness caused by missed detections of small objects and occlusions among dense objects in road scenes, a YOLOv8-RC object detection algorithm is proposed. First, by introducing receptive field attention convolution, the network can flexibly adjust the level of attention to targets of different scales, thereby enhancing the feature extraction capability of the backbone network and improving detection accuracy. Secondly, a lightweight CARAFA is used to replace the upsampling operator in the neck network, which reduces the number of parameters while improving detection accuracy. Finally, by adding a small object detection layer, local feature extraction is performed on larger-sized feature maps, thereby improving the accuracy of small object detection. Experimental results show that on a dataset excerpted from BDD100K, the average precision of the YOLOv8-RC algorithm model in this study increased by 5.9% compared to the model before improvement, with the model's parameter count reduced by 0.05M. © 2025 SPIE.

关键词： Object detection Roads Target detection detection and tracking algorithms Performance modeling Feature extraction Data modeling Small targets Convolution Education and training

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Review of identification technologies for stray animal governance 5

Review of identification technologies for stray animal gover...

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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Han, Puze College of Science and Engineering University of Minnesota Twin Cities Minneapolis55455 United States

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In recent years, societal changes have led to a growing prominence of pets in people's lives. However, uncontrolled pet reproduction in urban areas has given rise to a significant issue of stray animals, posing serious threats to human health and the environment. Conventional manual methods for counting stray animals face challenges in terms of efficiency and the risk of disease transmission. With technological advancements, image recognition, and sound identification, among other techniques, have emerged as crucial tools to address this issue. Image recognition, leveraging intuitive statistics based on external features, combined with the low-power attributes of sound identification and the health assessment capabilities of thermal imaging, collectively provide comprehensive technological support for stray animal population statistics. In the realm of image algorithms, both traditional target detection algorithms and deep learning methods such as RCNN and Faster RCNN employ convolutional neural networks to accurately identify and locate stray animals. Regarding sound algorithms, traditional Gaussian mixture models and hidden Markov models, as well as deep learning techniques involving convolutional neural networks, have effectively enhanced the accuracy of stray animal sound recognition. The integration of image and audio in a hybrid method significantly enhances stray animal monitoring. Employing advanced techniques in video tracking and sound recognition, this approach offers an efficient and practical solution, crucial for wildlife ecosystem surveillance and conservation. Research indicates that the application of deep learning methods in the domains of image and sound has significantly advanced compared to traditional approaches. In terms of image processing, I utilized the YOLO algorithm to perform grid division, feature extraction, and loss computation steps to achieve stray animal detection, demonstrating outstanding performance. Through the application of t

关键词： Animals detection and tracking algorithms Object detection Animal model studies Image processing Education and training Diseases and disorders Positron emission tomography Deep learning Expectation maximization algorithms

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CS-YOLO: A YOLOv5-based SMT defect detection algorithm 4

CS-YOLO: A YOLOv5-based SMT defect detection algorithm

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4th International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2024

作者： Zhang, Huayu Mao, Pujun Wu, Zongkun Shandong University of Science and Technology 579 Qianwangang Road Shandong Qingdao China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

At present, SMT defects have problems such as dense distribution of small components and similar and overlapping characteristics among defects, which lead to low accuracy of defect detection. It is proposed to use CS-YOLO defect detection algorithm to improve the SMT detection accuracy. Firstly, based on the YOLOv5 model, the SPD-Conv module is used to enhance the extraction ability of pixel feature information in the pooling process;secondly, the CBAM module retains important feature information from channel attention and spatial attention to improve the perception ability of the model to represent different degrees of features;the MPDIoU is used to calculate the loss function, reduce the distance between the target box and the detection box and reduce influences of additional redundant information, and to improve the efficient positioning and classification of small target positions. Finally, the experimental data show that the mAP, Recall rate and accuracy of the algorithm are somewhat improved compared with the original algorithm, and they are deployed in the actual PCB production process. This algorithm significantly enhances the performance and detection rate of SMT small targets, demonstrating its effectiveness. © 2025 SPIE.

关键词： Defect detection detection and tracking algorithms Target detection Small targets Education and training Feature extraction Data modeling Deep learning

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Tobacco Shred Varieties detection based on Yolov7

Tobacco Shred Varieties Detection based on Yolov7

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Ran Guo, Zhongya Liu, Xin Guan, Wenfeng Li, Huanwei Zheng, Junyue Guangdong China Tobacco Industry Co. Ltd. Guangzhou510000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The composition distribution detection of processed tobaccos is one of the main difficulties in cigarette design and processing. The radio of the four kinds of tobacco influences the quality of cigarettes. Therefore, it is very important to detect tobacco shreds on the tobacco processing in order to ensure the cigarette consistency of formulation. Aiming at solving the problems of poor efficiency and low accuracy of traditional method and utilizing the advantages of deep leaning network in object detection, a method based on Yolov7 for tobacco shred detection is proposed. The collected images of four categorized tobacco shreds are manually marked, and model is trained through Yolov7 network. The mean average precision of tobacco shred detection in this work reached 0.986, and the tobacco shred detection speed was also vastly increased compared to traditional manual detection method. © 2025 SPIE.

关键词： Object detection Education and training Data modeling Convolution Feature extraction Deep learning detection and tracking algorithms Head Performance modeling Statistical modeling

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Comparative Study on Machine Learning Algorithm Prediction of Soybean Protein Localization

Comparative Study on Machine Learning Algorithm Prediction o...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Liu, Zhiqiang Tang, Yulan Chongqing Sinokonjac Biotechnology Co. Ltd. China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Soybean protein is one of the important components of modern human diet. How to accurately and quickly determine the location of soybean protein is the main topic of research scholars. In order to understand the soybean genome, improve human dietary health behavior, explore effective methods of soybean protein subcellular localization prediction with different deletion degrees, and improve the level of soybean protein subcellular localization prediction, this paper aims to understand the current situation of machine learning algorithms and applications and, according to the research and application characteristics of protein localization technology in the new era, This paper mainly discusses the application advantages of four kinds of machine learning algorithms in predicting soybean protein localization, so as to provide technical support for soybean protein data prediction analysis. © 2025 SPIE.

关键词： Proteins Machine learning Evolutionary algorithms detection and tracking algorithms Biological samples Decision trees Random forests Lithium Analytical research Data modeling

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Enhanced multi-target detection in automotive FMCW LiDAR sensors affected by phase noise 12

Enhanced multi-target detection in automotive FMCW LiDAR sen...

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Photonic Instrumentation Engineering XII 2025

作者： Santacruz, Javier Pérez Romme, Jac Zhang, Xuebing Araujo, Esteban Venialgo Dahlem, Marcus Oldenbeuving, Ruud M. IMEC-NL Holst Centre Eindhoven5656 AE Netherlands IMEC Kapeldreef 75 Leuven3001 Belgium

ISBN: (纸本)9781510684942

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) systems offer unprecedented sensing features, being a crucial block in both present and future automotive industries. However, phase noise impairment is unavoidable in most FMCW LiDAR sensors, deteriorating their multi-target detection capabilities. Therefore, this work proposes, analyzes, and studies a novel and low-complex technique for mitigating phase noise in multi-target FMCW LiDAR systems. Experimental results show excellent performance in terms of detection probability of the proposed algorithm, highlighting it as a key component for enabling scalable and efficient autonomous vehicle sensors. © 2025 SPIE.

关键词： LIDAR Digital signal processing Interference (communication) detection and tracking algorithms Unmanned vehicles Optical fibers Automotive driver assist sensors Frequency modulation

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Object-centered heuristic A* conformance checking method 2

Object-centered heuristic A* conformance checking method

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yang, Xiaolong Wang, Lili College of Mathematics and Big Data Anhui University of Science＆Technology Anhui Huainan232001 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Anhui Huainan232001 China

ISBN: (纸本)9781510689053

Traditional approaches typically flatten the process when checking the conformance of complex processes. However, this flattening approach can result in the loss of dependencies between objects, reducing the accuracy of the conformance check. This paper proposes an object-centered alignment optimization method based on the heuristic A* algorithm to address this issue. This method effectively improves the efficiency of object-centered alignment in complex scenarios by considering both heuristic search and actual cost. The experimental results demonstrate that, in comparison to existing approaches, the proposed method presented in this paper observably enhances the efficiency of alignment in complex graph structures, exhibits superior adaptability, and gains a better understanding of the relationship between object-centric process behavior and data. These results provide new ideas and methods for improving conformance checking, which are of great theoretical and practical significance, especially in dynamic and complex environments where the potential for application has been further validated. © 2025 SPIE.

关键词： detection and tracking algorithms Mining Alignment modeling Process modeling Algorithm development Beam propagation method Mathematical optimization Connectors Medicine Transportation

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Based on improved YOLOv7 tomato fruit recognition method in natural environment 4

Based on improved YOLOv7 tomato fruit recognition method in ...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Fang, Xuanfeng Wen, Yong College of Artificial Intelligence Guangxi Minzu University Nanning530006 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Traditional methods for tomato fruit recognition and detection face challenges in natural environments, including low recognition accuracy and slow processing speeds. To achieve automated and accurate recognition of tomato fruits in complex environments, thereby facilitating automatic fruit picking, this study proposes a method based on an enhanced YOLOv7 model for tomato fruit recognition. This approach modifies the original YOLOv7 model by integrating SEnet structures into the ELAN module within the backbone network, allowing for the assessment of the importance of different channel features. Additionally, a global attention GAM module is incorporated at the Neck end of the model to improve the network's feature representation capabilities. Experimental validation demonstrates that the improved YOLOv7 model performs satisfactorily on the test set, achieving a mean accuracy of 98.9%. Compared to the original YOLOv7 model, the enhanced version exhibits a mean average precision increase of 6.2 percentage points. The comprehensive results indicate that the improved YOLOv7 model offers robust technical support and insights for the realization of automated tomato fruit picking. © 2025 SPIE.

关键词： Object detection detection and tracking algorithms Target detection Performance modeling Statistical modeling Target recognition

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