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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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Swin Detr: Pedestrian and vehicle detection under low light conditions based on Swin Detr 24

Swin Detr: Pedestrian and vehicle detection under low light ...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Huang, Yifei Pan, Kaixin Wei, Kewei Wu, Ruirui School of Science Beijing University of Civil Engineering and Architecture Beijing China School of management and engineering Capital University of Economics and Business Beijing China

ISBN: (纸本)9798400707308

Currently, deep learning-based object detection algorithms can rapidly and accurately locate vehicles and pedestrians under normal conditions. However, in low-light environments, the detection accuracy of these algorithms significantly declines due to image blur and the interference of complex backgrounds. To address the issue of poor detection robustness in low-light conditions, this paper proposes Swin DETR to meet the demands of object detection algorithm research in such environments. Additionally, a comparison will be made with Fast RCNN and YOLO v7. This research holds significant importance and research value for expanding the application scenarios of autonomous vehicles. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Dense object detection for remote sensing images based on multi-scale partitioning and super-resolution optimization 24

Dense object detection for remote sensing images based on mu...

引用

3rd international conference on Signal processing, Computer Networks and Communications, SPCNC 2024

作者： Li, Lu Zhu, Xia Ni, Shaofeng Gao, Fan State Grid Location based Service Co. Ltd. Beijing China State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400710834

Detecting dense objects in remote sensing image is a critical yet challenging task due to variations in target scale, image resolution, and complex backgrounds. Traditional detection methods often struggle with these dense regions, particularly for small or overlapping objects. To overcome these limitations, this study introduces an innovative approach that combines multi-scale partitioning with super-resolution optimization to improve the detection of densely packed objects. By applying K-Means clustering to identify regions of interest (ROI) and using a local super-resolution network to enhance these areas, we improve the clarity and detectability of small-scale objects. Additionally, we implement a global-local feature extraction architecture, where global features are captured using Transformer-based attention, while local features focus on fine-grained object details. Non-Maximum Suppression (NMS) is employed to fuse multi-scale features and eliminate redundant detections. Experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of our approach, achieving superior performance over state-of-the-art methods in detecting small and densely packed targets. This method shows significant promise for remote sensing applications requiring high-precision target detection. © 2024 Copyright held by the owner/author(s).

关键词： Proximity sensors

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Vision-Guided Robotic System: image processing and Kinematic Integration 3rd

Vision-Guided Robotic System: Image Processing and Kinematic...

引用

3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Wan, Changcheng School of Geospatial Engineering and Science Sun Yat-sen University Zhuhai China

ISBN: (纸本)9789819791279

This paper explores the development and implementation of a sophisticated vision-guided robotic manipulation system, underpinning advancements in manufacturing, healthcare, and service robotics through the integration of image processing and kinematic control. The system employs a serial-link robotic arm equipped with a high-resolution camera, facilitating real-time environment interaction through precise object tracking and manipulation. Central to the research is the design of the robotic arm’s kinematic model, the application of advanced image processing for object detection and tracking, and the formulation of control strategies translating visual data into accurate manipulative actions. The study underscores the significance of merging image processing with robotic control to adapt dynamically to changing environments, leveraging techniques such as background subtraction, median filtering, and binary thresholding for efficient object isolation. The implementation of kinematic equations enables the calculation of the robotic arm’s movements to interact with objects accurately, considering spatial positioning and geometric constraints. Experimental results demonstrate the system’s efficacy in enhancing operational efficiency and flexibility in diverse application scenarios, achieving over 95% tracking accuracy and rapid response times. This research contributes to the robotic manipulation field, highlighting the potential of combining image processing and kinematic control to innovate robotic system designs, with broad implications for industrial automation and assistive technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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A small target detection method based on different scales for unmanned aerial vehicles 3

A small target detection method based on different scales fo...

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3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Qi, Xiuyuan Wang, Zheng Liu, Zherong Wei, Haojie Hao, Shuang Zhai, Yufei Liang, Qing Liu, Ye University of Electronic Science and Technology of China Chengdu China AVIC Chengdu Aircraft Design & Research Institute Chengdu China

ISBN: (纸本)9781510688179

With the continuous iteration and development of computer vision technology, intelligent applications and devices centered around this technology are gradually playing an increasingly important role in human daily life and military operations. Among these, technologies based on object recognition and detection are widely applied in fields such as robotics, drones, and aircraft combat. These areas require precise localization information provided by object detection technology to achieve functions like navigation or attack. However, due to the characteristics of the targets being detected, their scale changes according to their distance, resulting in significant scale variations in images for the same target. Timely detection of distant targets is crucial for aircraft control and early warning, making small target recognition a key factor limiting the widespread application of recognition algorithms. To address this issue, this paper processes the original images based on color space level, global level, and pixel level, proposing a small target recognition method for RGB images. This method involves separately processing different color spaces of the RGB image, followed by information fusion, analysis of the entire image and local regions, and pixel-level filtering in local areas. Experimental results demonstrate that our algorithm can accurately recognize targets of varying sizes and observation angles. © The Authors.

关键词： Information fusion

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Improved UAV-to-Ground Small object detection Algorithm Based on Yolov5s 3rd

Improved UAV-to-Ground Small Object Detection Algorithm Base...

引用

3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Luo, Ling Wu, Zhang Han, Wei Sun, Xuefei Bai, Tingting Army Engineering University of PLA Jiangsu Nanjing210001 China

ISBN: (纸本)9789819624676

Small objects make up a high proportion of Unmanned aerial vehicle (UAV) images, but the existing detection algorithms have problems such as low detection accuracy and high leakage rate. To improve small object detection, an improved YOLOv5s model is proposed in this paper. In the Neck Network of the YOLOv5s model, a channel attention module attention (CBAM) mechanism is added. By adding CBAM after each CSP2_1 module and before the convolution operation, the model can automatically ignore non-critical information and focus highly on key features in the image. The improved YOLOv5s model was evaluated on the VisDrone2019-DET dataset and its performance was compared with YOLOv5s and YOLOv8s. The YOLOv5s-CBAM model has improved mAP_0.5 by 2.2% and mAP_0.5:0.95 by 1.2%. The loss convergence speed is faster and the stability is better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： object tracking

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Faster-RCNN in Human Detecting on Thermal images 3rd

Faster-RCNN in Human Detecting on Thermal Images

引用

3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Anh, Le Tuan Ngan, Tran Thi Dung, Vu Viet Luc, Do Dinh Nguyen, To Huu Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam International School Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9783031809422

By the development of Artificial Intelligence, many applications of deep learning and computer vision algorithms in object detection on color images were introduced. The obtained results get higher accuracy than other classical methods. However, detecting objects on thermal images has not received much research attention. In this paper, we focus on researching and proposing the use of Faster-RCNN deep learning model in order to detect people on thermal images. The proposed model consists of two phases: Data processing and model applying. The experiments on a thermal image dataset, namely as Deep Thermal Outdoor, are performed to evaluate performance of the proposed model and compare it to YOLO and SSD models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A Novel image Segmentation Based on Hybrid Dilated Convolutional Neural Networks and Optimized IoU 3rd

A Novel Image Segmentation Based on Hybrid Dilated Convoluti...

引用

3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhu, Ziyue Li, Yijun Hou, Jiarui Xie, Fei Zhao, Jing Ma, Baoping Ma, Lei School of Electrical and Automation Engineering Nanjing Normal University Nanjing210023 China The College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing Wanhe Robot Technology Co. Ltd. Nanjing211106 China

ISBN: (纸本)9789819624676

To address the issue that instance segmentation models often prioritize speed over accuracy, leading to frequent detection errors or mask omissions, we propose a novel image Segmentation founded on hybrid dilated CNN and optimized IoU. Firstly, we enhance the backbone network using a hybrid dilated convolutional neural network to improve feature extraction capabilities, mitigating the loss of continuity information. Secondly, we introduce an objection localization and segmentation loss function built upon an optimized IoU metric. This algorithm ensures that the optimized IoU does not set to zero even when objects do not overlap, thereby addressing the challenge of gradient optimization. Additionally, we employ a Soft-NMS algorithm to enhance object localization accuracy and resolve issues related to mask omissions. Finally, we utilized the MS COCO dataset and conducted experiments under an Intel Core i7-9900K CPU and NVIDIA Titan RTX GPU environment. Our proposed algorithm has proven to be superior to prevalent instance segmentation models like FCIS, Mask R-CNN, and YOLACT, showing better performance in both speed and accuracy, as indicated by the results. Specifically, comparing to the FCIS model, FPS increased by 6.4, and the accuracy rate improved by 6.3%;compared to the Mask R-CNN model, FPS enhanced by 27.1, and the accuracy rate reduced by 0.4%;compared to the YOLACT model, FPS decreased by 2.3. The average detection and segmentation time for our algorithm was 29.9 ms, with an instance segmentation accuracy of 96.7%. Overall, our proposed algorithm offers superior performance, effectively balancing the need for real-time processing with high accuracy in instance segmentation tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Investigating Salient object detection Methods Tailored for Edge Computing Infrastructure 3rd

Investigating Salient Object Detection Methods Tailored for...

引用

3rd international conference on Computing, Communication and Learning, CoCoLe 2024

作者： Sebastian, Elsa Martin, John Paul Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam686635 India

ISBN: (纸本)9783031790409

Salient object detection aims in identifying the most significant parts of a scene as viewed by the human eye and plays a vital role in a variety of applications, including object detection, video compression, and augmented reality. Saliency detection has emerged as a fundamental preprocessing step in many computer vision and image processing tasks. Implementing saliency detection in edge computing platforms is challenging, however it promises enhanced real-time responsiveness, reduced bandwidth usage, and improved privacy. This paper examines the most advanced techniques for adapting saliency detection techniques to edge computing and discusses encountered challenges. The paper also gives insights into the evaluation metrics that can be utilized for performance assessment, investigates primary application domain and proposes potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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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...

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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...

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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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