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检索条件"任意字段=2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024"

共 128 条记录，以下是1-10 订阅

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Research on tree detection method based on UAV LIDAR point cloud 3

Research on tree detection method based on UAV LIDAR point c...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Yu, Wenquan Song, Dalei School of information Science and Engineering Shenyang University of Technology Shenyang110870 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

As one of the most important natural resources, forest plays an irreplaceable role in human development and survival. Most of the traditional tree detection methods are artificial field surveys. This paper proposes to use a UAV equipped with LIDAR to fly on site to collect tree point cloud information, and then use deep learning method to extract forest tree feature information and segment trees by PointNet network, so as to achieve tree detection effect. © 2024 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Improved pointpillar point cloud object detection based on channel attention mechanism 3

Improved pointpillar point cloud object detection based on c...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Tu, Gao Geng, Yingnan Jilin Institute of Chemical Technology Jilin China Changchun Institute of Technology Jilin China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

To upgrade the exactness of the 3D point cloud target location calculation, this investigate presents an progressed PointPillars strategy leveraging spatial consideration and channel components. At first, we coordinated a refined point cloud include representation into the column include arrange, upgrading the coding of highlights and making strides the uniqueness of each point's representation. Secondly, we join a spatial consideration component into the pseudo-image handling. This method recalibrates the highlight weights of spatially encoded focuses, subsequently boosting the algorithm's capability to extricate basic highlights and upgrading location performance. Experimental approval on the broadly utilized KITTI dataset illustrates critical enhancements in discovery exactness and soundness compared to the initial calculation. © 2024 SPIE.

关键词： object detection

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detection and feature fusion of trajectory Poisson Multi-Bernoulli mixture algorithms for Visual Multi-object tracking 3

Detection and feature fusion of trajectory Poisson Multi-Ber...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Zhang, Guochen Shen, Qinmu Lu, Zhejun College of Electronic Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In a video multi-object tracking scene, the problems of target occlusion, track crossing still exist. Limited by the quantity of the detector, the traditional association algorithms will lead to missing and false tracking and increase the number of target ID switch and trajectory fragmentation. Aiming at these problems, this paper proposes an optimized algorithm based on the framework of Trajectory Poisson Multi-Bernoulli Mixture Filter, combines the joint detection and embedding algorithm to output both targets’ detections and features, designs new association and update algorithm to improve the trajectory maintenance. The result shows that the proposed algorithm can effectively decrease the number of ID switch and maintain the trajectories when tracking with short-time occlusions and crossing. It is tested on MOT datasets and gets a positive effect compared with other algorithms. © 2024 SPIE.

关键词： Embeddings

来源：评论

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Construction consumption measurement method based on convolutional cloud image recognition technique 3

Construction consumption measurement method based on convolu...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Zhou, Lili Yong, Liu Chengdu Engineering Corporation Limited POWERCHINA Chengdu China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

This study aims to explore the construction consumption determination method based on convolutional cloud neural network image recognition technology. By analyzing the application of convolutional cloud neural network in the field of image recognition and combining the actual situation and data collection at the construction site, a construction consumption determination method based on convolutional cloud neural network is proposed. By processing and analyzing the image data of the construction site, combined with the deep learning algorithm, accurate determination of the consumption of various materials and resources in the construction process is realized. The results show that the construction consumption measurement method based on convolutional cloud neural network image recognition technology has high accuracy and practicability, and can provide effective support for construction management and cost control. © 2024 SPIE.

关键词： Convolutional neural networks

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Design of a book misplacement detection system based on image recognition 3

Design of a book misplacement detection system based on imag...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Shi, Qiyuan Fan, Fenglong Li, Ying School of Information Science and Engineering Dalian Polytechnic University Dalian China Dalian Cloud Force Technologies Co. Ltd. Liaoning Dalian China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

With increasing library visits, book misplacement has become a significant issue, affecting over 5% of books globally and burdening library staff. To address this, we developed a system using YOLOv8 with an enhanced CBAM (Convolutional Block Attention Module) for spine segmentation. The HSV (Hue, Saturation, Value) algorithm segments the book label area, and Optical Character Recognition (OCR) is used to extract call numbers. Book misplacement is detected using ASCII codes, and results are presented to library staff. Experimental results show that this approach significantly improves book spine recognition accuracy compared to traditional methods and better meets users' needs. © 2024 SPIE.

关键词： Optical character recognition

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Multi view and multi description video coding based on pattern and prediction vector replication 3

Multi view and multi description video coding based on patte...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Wang, Xiaolan The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province Xiamen Institute of Technology China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

Although the existing Multi View Video Coding (MVC) achieves high coding efficiency, it also increases the risk of error propagation. This article will study the multi view and multi description video encoding technology from the perspective of improving fault tolerance, and propose a multi view and multi description video encoding structure based on pattern and prediction vector reuse. The experimental results show that the proposed multi view and multi description video encoding algorithm based on pattern and prediction vector reuse can achieve the goal of significantly reducing encoding complexity while maintaining high encoding efficiency, its performance is superior to other existing algorithms. © 2024 SPIE.

关键词： Video signal processing

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Third international conference on image processing, object detection, and tracking, ipodt 2024

Third International Conference on Image Processing, Object D...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

ISBN: (纸本)9781510685512

The proceedings contain 35 papers. The topics discussed include: design of a book misplacement detection system based on image recognition;automatic detection of ultrasonic water meter LCD pattern elements based on ConvNeXt;deep intrinsic image popularity assessment by learning to rank;a late fusion framework for multi-vehicle collaborative perception;subpixel precision BGA chip localization method based on Lagrange interpolation;signal-to-noise ratio analysis and experimental verification of imaging laser detection system;comparison of cell image segmentation results based on U-Net and transformer;and deep learning based algorithm for classifying pedestrian behavior at crosswalks.

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Signal-to-noise ratio analysis and experimental verification of imaging laser detection system 3

Signal-to-noise ratio analysis and experimental verification...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Sun, Yingping Wang, Dong Bai, Dengtian Liu, Yan Miao, Rongzhen Lanzhou Institute of Physics Lanzhou730000 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In light of the escalating threat posed by lasers, there is a growing imperative to investigate high-performance laser detection systems. When juxtaposed with non-imaging alternatives, imaging laser detection systems offer the distinct advantage of superior angular measurement accuracy. However, they concurrently impose heightened demands on system efficacy. This study undertakes theoretical computations pertaining to the pivotal technical parameters and signal-to-noise ratio of imaging laser detection systems. It furnishes the methodologies and outcomes of these calculations, subsequently subjecting them to empirical validation on the ground. Through experimentation, the validity of the established theoretical computation approach was affirmed, highlighting its utility in informing the optical system design and detector selection processes of laser detection systems. This integrative approach bears significant implications for advancing the overall performance inquiry of such devices. © 2024 SPIE.

关键词： Signal to noise ratio

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The application and research of improved YOLOv10 algorithm in smoking behavior detection on campus 3

The application and research of improved YOLOv10 algorithm i...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Lu, Li Xi'an Eurasia University Xi'an China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In order to maintain a good living and learning environment and students' health in campus, it is particularly important to detect students' smoking behavior in campus. With the continuous development of artificial intelligence, object detection algorithms based on computer vision are becoming increasingly perfect. However, due to the small size of the cigarette target, existing object detection algorithms have serious missed detection and false detection phenomena, and are difficult to use in a real campus environment. Based on the YOLOv10 object detection algorithm, this paper proposes a multi-level end-to-end detection model M-YOLOv10. Aiming at the limited feature extraction capabilities of the original YOLOv10, a self-attention mechanism is introduced to improve the model's global feature extraction capabilities. At the same time, the feature fusion module is improved to achieve multi-level fusion and improve the model's small object detection capabilities. Experimental results show that compared with the model before improvement, the M-YOLOV10 algorithm improves mAP@0.5 by 17.6%, and the model detection speed remains basically unchanged. © 2024 SPIE.

关键词： Students

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Small crop detection based on improved YOLOv8 algorithm for robotic pollination 3

Small crop detection based on improved YOLOv8 algorithm for ...

引用

2024 3rd international conference on image processing, object detection, and tracking, ipodt 2024

作者： Zhang, Zizhe Yin, Jianjun School of Information Science and Technology Fudan University Shanghai200433 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

Traditional object detection algorithm YOLOv8 faces challenges when robotic pollination needs to identify crops, such as misdetection and omission due to the small size of the crop, dense planting, vegetation cover and background confusion. To address these issues, an improved algorithm based on YOLOv8 is proposed to enhance detection performance. First, the small object detection layer is added to enhance the learning ability of feature information of small objects. Second, BiFPN is used to replace FPN and PAN for multi-scale feature fusion. Finally, two existing difficult sample mining methods, Loss Rank Mining (LRM) and focal loss (FL), are introduced to adjust the optimized loss function and achieve difficult sample mining to improve the detection of small crops. Comparative and ablation tests are carried out using tomato flower image datasets derived from a simulated autonomous pollination robot sensing system in order to verify the efficacy of the revised method. The results demonstrate that the improved YOLOv8 algorithm enhances the average accuracy by 9.787% and achieves more accurate detection of small crop. © 2024 SPIE.

关键词： Fruits

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