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

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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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Research on photovoltaic power tracking based on IGWO-INC algorithm 4

Research on photovoltaic power tracking based on IGWO-INC al...

引用

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

作者： Gao, Chuang Ma, Hongwei School of Business Shanghai Dianji University Shanghai201306 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

The P-U characteristic curves of PV arrays under localized shading conditions may exhibit multiple peaks. Conventional approaches are prone to encountering the local maximum power point (LMPP) and face challenges in effectively balancing the trade-off between speed and precision inherent in maximum power point tracking (MPPT) techniques. A hybrid IGWO-INC algorithm is proposed in this paper, which combines the Improved Gray Wolf (IGWO) algorithm with the conductivity incremental method (INC) to effectively search for the vicinity of the maximum power point (MPP) through velocity and position updates, thereby achieving accurate tracking of local maximum power points. The final simulation results demonstrate that the IGWO-INC algorithm enhances tracking speed and accuracy, reduces power oscillations, and ensures normal operation. The validity of the algorithm has been verified. © 2025 SPIE.

关键词： detection and tracking algorithms Photovoltaics Optical power tracking algorithms Solar cells Particle swarm optimization Computer simulations Solar energy Mathematical optimization

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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...

引用

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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Vehicle target detection under adverse weather conditions based on improved YOLOv8 4

Vehicle target detection under adverse weather conditions ba...

引用

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

作者： Li, Fuhao Meng, Lingyi College of Transportation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510686304

To address the issues of low detection accuracy and poor real-time performance of autonomous driving target detection algorithms under adverse weather conditions, this paper proposes an improved vehicle target detection algorithm, MFWYOLOv8, specifically designed for such environments. The C2f module in the backbone network is optimized by incorporating the MSBlock, which reduces computational complexity and achieves a lightweight model. The SPPF module is replaced with the Focal Modulation (FM) module to automatically focus on critical regions of the target, enhancing the accuracy and robustness of detecting hard-to-identify small objects. Furthermore, the WIOUV2 Loss function is employed to optimize the original network's loss function, thereby improving the model's generalization capability. Experimental results on the improved BW8K dataset demonstrate that the enhanced YOLOv8 algorithm achieves a 2.5% increase in mAP@0.5 compared to the original algorithm, with a 3.1% reduction in the number of parameters and a 6.8% reduction in computational complexity. Additionally, the mAP@0.5 under three adverse weather conditions (rain, snow, and fog) shows respective improvements of 1.3%, 1.7%, and 2.7%, demonstrating the effectiveness and advancement of the improved algorithm. © 2025 SPIE.

关键词： Adverse weather detection and tracking algorithms Target detection Object detection Data modeling Rain Performance modeling Frequency modulation Modulation

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Optimized 3D object detection algorithm for complex road scenes 4

Optimized 3D object detection algorithm for complex road sce...

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

作者： Chen, Tingshuai Yuan, Ye Zheng, Yi College of Arts and Sciences Beijing Institute of Fashion Technology Chaoyang Beijing100020 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In response to the weakness of traditional 3D point cloud object detection algorithms in detecting small targets with low accuracy, an improved PointPillars method based on spatial attention mechanism is proposed. After the formation of the pseudo-image by the pillar feature network, a GE (Gather-Excite) module is added. This module first allows the feature map to obtain spatial information through Gather, and then matches this spatial information with the input through Excite to produce attention information, using feature context to enhance the expressive power of convolutional neural networks. Finally, the improved algorithm is verified using the public dataset KITTI. The experimental results show that this method can accurately detect small targets such as pedestrians and cyclists, and also has significant effects on car categories. Compared with the benchmark model, the 3D average precision (AP) of the three target categories under medium detection difficulty conditions has been increased by 0.71%, 1.23%, and 2.15%, respectively, proving the effectiveness of the proposed improvement. © 2025 SPIE.

关键词： Object detection detection and tracking algorithms Point clouds 3D modeling Target detection Feature extraction 3D acquisition Data modeling Convolutional neural networks Performance 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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Learning Discontinuity in Orientation with Cosine Metric in Real-Oriented Object detection 8

Learning Discontinuity in Orientation with Cosine Metric in ...

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

作者： Lu, Xin Kuang, Guowen Wu, Zhitao Wang, Xu Yang, Jinfeng Chen, Qiuxia Liu, Xiaohua Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China School of Electronics and Information Engineering University of Science and Technology Liaoning Liaoning 114045 China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

The real-oriented object detection has the advantage of indicating the inherent orientation of the target object, which has numerous downstream applications in the context of industry and stock farming. However, the discontinuity point at zero angle on orientation exists, severely affecting the detection of objects with near-zero orientations. In this paper, we propose a cosine-metric-based loss on orientation and implement one ROBB detector by extending the PP-YOLOE-R framework. Extensive experiments are conducted to compare with the state-of-the-art oriented object detection algorithms and validate the effectiveness of our proposed method. The results have shown that the detector with our proposed cosine-metric-based loss achieves the highest AP of 97.69 on the Cattle-ROBB dataset, significantly outperforming other-oriented object detectors. The stability of orientation prediction is also studied, and it indicates that our method is more stable and converges with the lowest error in orientation during the training process. Our method also performs best on the modified dataset which contains only objects with near-zero orientations. All these experimental results have demonstrated that the cosine-metric-based loss is a superior solution to the discontinuity issue in real-oriented object detection. © 2025 SPIE.

关键词： Object detection Discontinuities detection and tracking algorithms

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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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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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Design of angle correction algorithm for robot chip assembly based on optical vision inspection and analysis 2

Design of angle correction algorithm for robot chip assembly...

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

作者： Duan, Honggang Xue, Rui Dong, Bin Wang, Fengze Niu, Anhao Shandong Vocational College of Science and Technology Shandong Weifang261053 China

ISBN: (纸本)9781510689053

In the process of assembling chips onto circuit boards, due to the arbitrary transportation of chips in the production line, their angles show arbitrary changes. Currently, six axis robots are widely used for chip assembly. How to make the robot accurately correct the chip angle according to the characteristics of the assembled chip without singular points is an important challenge in the chip assembly process, and it is also the key to improving chip assembly accuracy and reducing product defect rates. The research is based on multi scene optical vision inspection of chips, analyzing chip types and calculating chip placement angles. The detection information is transmitted to the robot controller through Socket communication. At the same time, a chip angle correction optimal solution algorithm is designed by combining the maximum angle posture of the robot rotating along the Z-axis of the end assembly tool. This algorithm enables the robot to quickly identify and accurately correct any chip type and angle without posture singularities, solving the problem of difficult chip angle correction and improving chip assembly accuracy and product production efficiency. © 2025 SPIE.

关键词： Chip manufacturing Transistors Design Integrated circuits Cameras Visualization detection and tracking algorithms Inspection Calibration Visual analytics

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