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

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Research on 3D point cloud registration algorithm based on FPFH and ColorICP

Research on 3D point cloud registration algorithm based on F...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Pan, Junjie Zhang, Xu School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

With the rapid development of 3D reconstruction, point cloud registration technology, a key step in 3D data processing, has garnered significant attention. Existing 3D point cloud registration technologies face issues such as low matching rates in coarse registration, misidentification of feature points, lengthy registration times, and low registration accuracy. Consequently, an improved registration algorithm that combines Fast Point Feature Histogram (FPFH) for coarse registration and Colored Iterative Closest Point (ColorICP) for fine registration has been proposed. The process begins with pre-processing the point cloud through downsampling filtration;next, point cloud features are extracted using FPFH to achieve feature matching and obtain the initial transformation matrix;finally, ColorICP is used for fine point cloud registration. Experiments on the Stanford bunny dataset from the standard point cloud library and real-world point cloud data demonstrate that the proposed registration algorithm effectively utilizes both color and geometric features of the point cloud, achieving significant improvements in registration accuracy and duration compared to traditional registration algorithms. © 2025 SPIE.

关键词： Point clouds Image registration Histograms detection and tracking algorithms Reconstruction algorithms Nondestructive evaluation Picosecond phenomena Cameras RGB color model Algorithm development

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Research on target recognition, segmentation, and accurate positioning methods for obscured apple fruits under dense canopies

Research on target recognition, segmentation, and accurate p...

引用

2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Jiang, Houkang Liu, Jizhan Lei, Xiaojie Xu, Baocheng Wang, Jie Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment Jiangsu University Zhenjiang China Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu University Zhenjiang China Innovation Sub-Centre Jiangsu University Zhenjiang China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

In robotic harvesting, recognizing and locating target apple fruits, as well as detecting obstacles in complex environments like dense canopies, poses significant challenges. This paper presents a method for recognizing, segmenting, and accurately localizing obscured fruits. We employ instance segmentation to extract edge contours of fruits and branches, leveraging two-dimensional features for precise localization under occlusion and correcting picking point errors. Experimental results indicate that at a 10% occlusion ratio, the model achieves an average precision of 96.8%, an F1 Score for edge detection of 0.91, and a 3D reconstruction error of 3.1mm. Even at 50% occlusion, average precision remains at 88.3%, with an F1 Score of 0.80 and a reconstruction error of 7.6mm. These findings demonstrate that while occlusion significantly impacts 3D reconstruction, the algorithm retains high accuracy, fulfilling the precision requirements for fruit harvesting. © 2025 SPIE.

关键词： 3D modeling Target recognition Edge detection Image segmentation Robots 3D mask effects detection and tracking algorithms Education and training Robotics RGB color model

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Research on air combat engagement zone based on differential feedback golden section search algorithm 4

Research on air combat engagement zone based on differential...

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

作者： Xie, Lei Tang, Shangqin Wang, Yuan Zhou, Huan Air Force Engineering University Shaanxi Xi’an710038 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

An accurate and fast method for calculating the engagement zone of close-range air combat plays a crucial auxiliary role in maneuver decision-making and situational assessment. The existing methods using neural network fitting have poor portability for objects with different flight characteristics, and have high requirements for parameters. Therefore, this article improves on the traditional golden section method. Firstly, a target infrared detectable zone model is established to achieve detection estimation. Secondly, a differential dynamic feedback structure is introduced to improve the golden section search algorithm, overcoming the sensitivity of the initial parameters and reducing the calculation cycle. Finally, by estimating the target's escape maneuver, fast and accurate calculation of the air combat engagement zone is achieved. The simulation results show that the infrared detection zone model can correct the air combat engagement zone, and the improved algorithm can adapt to the calculation of engagement zone with high dynamic characteristics. © 2025 SPIE.

关键词： Target detection Infrared radiation Infrared detectors detection and tracking algorithms Unmanned combat air vehicles Aerodynamics Atmospheric sensing Signal to noise ratio Thermal modeling Infrared detection

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Tobacco Shred Varieties detection based on Yolov7

Tobacco Shred Varieties Detection based on Yolov7

引用

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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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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Research on dynamic network security detection technology based on SDN 4

Research on dynamic network security detection technology ba...

引用

4th International Conference on Network Communication and Information Security, ICNCIS 2024

作者： Zhao, Kai Beijing Polytechnic Beijing100176 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Traditional network architecture can no longer meet the development needs of technologies such as cloud computing and big data. SDN network architecture has the characteristics of high openness and programmability, which can quickly respond to changes in business requirements. SDN decouples the control management function of the network from the data forwarding function, simplifies network configuration work based on logic centralized controllers and open programming interfaces, thereby achieving a flat management mode and flexible data forwarding function. The centralized control and programmability of SDN networks also pose significant security threats to SDN. Based on the CSE-CIC-IDS2018 dataset, this article tests the performance of various intelligent learning algorithms, analyzes data flows, distinguishes normal data from abnormal data, improves detection accuracy, and has practical significance for the security defense of SDN networks. © 2025 SPIE.

关键词： Network security Computer security Network architectures Control systems Machine learning Data transmission Defense and security detection and tracking algorithms Information security Security technologies

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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

引用

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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Multi-attitude based target localization method

Multi-attitude based target localization method

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2024 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Yan, Suqing Xu, Xiaoyue Xiao, Jianming Ji, Yuanfa Hawari Bin Ghazali, Kamarul Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Science and Engineering Guilin University Guilin China National & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Centre for Advanced Industrial Technology University of Malaysia Pahang Al Sultan Abdullah Pekan Campus Pahang Pekan Malaysia

ISBN: (纸本)9781510689176

In recent years, location-based services have gradually penetrated all aspects of people's daily life, bringing great convenience to people's work and life. Pedestrian Dead Reckoning (PDR) is a positioning method that can derive a pedestrian's movement trajectory by relying on sensors information, with a high degree of autonomy and continuity. However, existing PDR tends to restrict the target posture to remain steadily pointing in the direction of pedestrian movement throughout the localization process. In addition, although it can achieve a high degree of positioning accuracy in a short period of time, it generates a cumulative error over time. To these problems, this paper proposes a fusion localization method that combines PDR based on multiple smartphones pose recognition and acoustic localization. In this method, we first employ a GWO-ELM based target posture recognition model. Then based on the posture recognition result for PDR localization, Time Difference of Arrival (TDOA) technique is used for acoustic localization. Finally, particle filtering is utilized to combine the two methods. To demonstrate the validity of the method, we performed several experiments on a motion trajectory with a length of 97.2 meters. The results show that the method has better localization performance than the traditional method. In addition, it can realize high-precision localization under different devices with universality and stability. © 2025 SPIE.

关键词： Acoustics Target recognition detection and tracking algorithms Particles Sensors Education and training Gesture recognition Satellite navigation systems Tunable filters Machine learning

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Comprehensive Latency Testing for Head-Mounted Displays 6

Comprehensive Latency Testing for Head-Mounted Displays

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Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) VI 2025

作者： Deveci, Murat Sarviluoma, Jani OptoFidelity Inc. United States

ISBN: (纸本)9781510686069

This paper presents a detailed analysis of latency performance testing in head-mounted displays (HMDs). By utilizing advanced robotics and algorithms, we identify subpar components and software impacting HMD performance, emphasizing their influence on user comfort and motion sickness. Our study employs a patented triggering board with precise localization and timestamping, measuring Angular and Linear Motion-to-Photon (MTP) Latency, jitter, drift, and repeatability. Additionally, we assess pixel persistence, refresh rate, video see-through latency, and hand controller accuracy. Results from commercial HMDs, including Apple Vision Pro and Oculus Quest 2, support improved R&D cycles and product iterations. © 2025 SPIE.

关键词： Head-mounted displays Cameras detection and tracking algorithms Sensors Aliasing Calibration Visualization Motion measurement Pose estimation Video

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