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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21-30 订阅

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3D-CA Net: A 3D-CNN with composite multidimensional attention for the assessment of lung aging 3

3D-CA Net: A 3D-CNN with composite multidimensional attentio...

引用

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

作者： Tian, Tangbin Pan, Jingshan Ge, Jing Li, Na Yu, Dexin Zuo, Liping Hu, Tao Liu, Shengyu Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Radiology Qilu Hospital of Shandong University Jinan China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

As the population ages, early diagnosis and treatment of lung diseases become increasingly important. Accurate assessment of aging-related changes in lung CT images is crucial for the prevention and treatment of related diseases. Traditional methods for lung aging assessment from CT images are time-consuming, subjective, and heavily reliant on the clinical experience of doctors. To address these issues, this paper proposes a lung aging assessment method with 3D-CA Net. The feature extraction part of the proposed network consists of four main 3D Convolutional and Composite Multidimensional Attention Modules. By introducing the Composite Multidimensional Attention Module, the advantages of spatial attention and self-attention are both utilized. Additionally, an improved E-cross-entropy loss function is employed to reduce overfitting and enhance generalization. Experimental results demonstrate that the 3D-CA Net significantly outperforms existing methods in terms of accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score. This work provides a comprehensive solution for lung CT image aging assessment and offers insights for future advancements in medical imaging analysis. © 2024 SPIE.

关键词： Lung cancer

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Multi-target tracking and detection on sea surface based on YOLOv5 3

Multi-target tracking and detection on sea surface based on ...

引用

3rd international conference on image processing and Media Computing (ICIPMC)

作者： Zhu, Jiaqi Zhang, Tao Jiangnan Univ Wuxi Peoples R China

ISBN: (纸本)9798350386660;9798350386677

As a large oceanic country, our country has abundant marine resources. The detection of marine objects plays an increasingly important role in marine economic activities. Due to the frequent incidents of sea area disputes in recent years, it is of far-reaching practical significance to improve my country's sea surface monitoring technology near the sea. Maritime target detection and tracking technology will play a huge role in the field of sea surface monitoring. This paper will take ships at sea as the main research object, and study the method of multi-target monitoring and tracking at sea based on deep learning. By improving the YOLOv5 algorithm, integrating multiple attention mechanisms, and matching the DeepSORT tracking algorithm, real-time detection and tracking of maritime targets is realized. Not only that but to achieve better detection results, this paper manually created a data set of maritime targets. Through the collection of data images such as ships, buoys, islands, and reefs, a rich data set of maritime targets has been established, which can be used for better training with algorithms.

关键词： maritime target detection YOLOv5 attention mechanism DeepSORT data set

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Research and application based on the improved YOLO V7 target detection algorithm 3

Research and application based on the improved YOLO V7 targe...

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

作者： Wang, Tenghui Zhang, Xiaofeng Ma, Yan Wang, Yapeng Xie, Haijun Zhu, Mingchao Su, Binghua Yao, Dong Beijing Institute of Technology Zhuhai519088 China Faculty of Applied Sciences Macao Polytechnic University 999078 China Zhuhai Kingsoft Office Software Co. Ltd Zhuhai519088 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

At present, YOLO-based of algorithms have been widely used in urban planning, traffic monitoring, ecological protection, military security and other fields, and their applicable scenarios are expanding. In view of the problems of high misdetection rate, high omission rate and insufficient accuracy in the image target detection task, this topic is dedicated to study the improved target detection algorithm based on YOLO V7. In order to optimize the time cost and computing resource consumption, the Anchor-free based design is introduced, and through optimizing the design of decoupling head, the independent processing of classification and regression tasks is realized to improve the efficiency of feature extraction. Based on this method, the CBATM attention mechanism is used to better capture the intercorrelations between features and improve the representational ability of the model. In the loss function section, this paper adopts the SimOTA method in YOLOX to realize the dynamic number allocation of positive samples, which greatly reduces the training time. Improved YOLO V7 target detection algorithm in the PASCAL VOC challenge public dataset VOC2007 data set, the results show that the improved YOLO V7 target detection algorithm than the original YOLO V7, has higher detection accuracy and efficient performance, the average detection accuracy (mAP) increased by 2.27%, compared with other classic target detection algorithm, the improved YOLO V7 performance is more accurate and efficient. © 2024 SPIE.

关键词： Urban planning

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End-to-End 3D object detection using LiDAR Point Cloud 3

End-to-End 3D Object Detection using LiDAR Point Cloud

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Raut, Gaurav Patole, Advait Univ Maryland Baltimore MD 21201 USA

ISBN: (纸本)9798350372977;9798350372984

Significant progress has been made in the field of autonomous vehicles. object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as input. Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. This approach does not implement a bird's eye view approach, which is generally applied for this application and thus saves the extensive pre-processing required. After studying the numerous networks and approaches, we have implemented a novel model intending to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in the form of 3D bounding boxes and labels of scene objects.

关键词： Autonomous vehicles

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A Review of Point Target and Extended Target tracking Algorithms 3

A Review of Point Target and Extended Target Tracking Algori...

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3rd international conference on image processing and Media Computing (ICIPMC)

作者： Sang Hairui Zheng Ran Cheng Huiyan Meng Xiaodi Li Lin Qi Jingya Beijing Inst Control Engn Space Optoelect Measurement & Percept Lab Beijing Peoples R China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386660;9798350386677

In this paper, we investigate the methods of multi-target tracking in imaging process, focusing on both point targets and extended targets. Point target imaging is characterized by low signal-to-noise ratio (SNR), small area, and indistinct appearance features. The study primarily introduces detection Before tracking (DBT) and tracking Before detection (TBD) algorithms, including Joint Probabilistic Data Association (JPDA), Multiple Hypothesis tracking (MHT), and Cardinalized Probability Hypothesis Density (CPHD) methods for data association, and analyzes the principles and application scenarios of TBD methods such as Hough transform, dynamic programming, and particle filtering. Regarding the problem of multi-extended target tracking, the study emphasizes the utilization of the Harris corner detection principle for feature extraction, the random hyper-surface model for modeling extended targets, and the trajectory establishment problem based on the Probability Hypothesis Density (PHD) algorithm.

关键词： point target extended target multi-target detection before tracking tracking before detection correlation filtering modeling tracking

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A Comparative Study of Environmental Perception and object Recognition Methods for Robots 3

A Comparative Study of Environmental Perception and Object R...

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3rd international conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)

作者： Fan, Haoyu Zhang, Guowei Zhou, Youtao Hu, Jinwen Xia, Yuntao Zhang, Shimian Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China Shanghai HRSTEK Co Ltd Shanghai 201102 Peoples R China

ISBN: (纸本)9798350352634;9798350352627

To address the selection of generic image segmentation and open-set object detection models for robotic object recognition, this paper experimentally compares the generic image segmentation and open-set object detection models, specifically, compares and analyzes the performance of the SAM (Segment anything Model) image segmentation model as well as its derivatives Mobile SAM and HQ-SAM generic image segmentation model, and the Grounding DINO and YOLO-world open set object detection models. The applicability of these state-of-the-art object recognition methods in different robotic environment perception tasks is fully validated through detailed experimental evaluations. The results show that Mobile SAM and YOLO-world perform well in real-time demanding scenarios and can meet the needs of fast response and processing, while HQ-SAM and Grounding DINO show superior performance in segmentation or detection tasks requiring high accuracy and are more suitable for application scenarios requiring high segmentation or detection accuracy. These findings provide references and guidance for the practical applications of robotic object recognition, and can help researchers and engineers to choose the most suitable models and methods in scenarios with different requirements.

关键词： component: Robotic object Recognition SAM Open-set object detection models

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Event-based detection and tracking 3

Event-based Detection and Tracking

引用

3rd international conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Liu, Mi Xiao, Ping School of Electronic & Information Engineering Guangzhou City University of Technology Guangdong Guangzhou510800 China

ISBN: (纸本)9798331506582

Due to the advantage of low latency, low power consumption, and high dynamic range of the event camera, it is not necessary to consider the blurring phenomenon of traditional cameras when doing target detection and tracking of moving objects. Therefore, the event camera-based multi-target tracking method generally results in better response time and accuracy[17] This report presents an object-tracking framework based on event count images. The framework contains a YOLO-based detector and multi-threaded single-object trackers based on the Kalman filter. In order to get an event count image dataset for YOLO model training and testing, a proposed event simulator is used to convert a traditional RGB image into a simulated event count image. The converted event image dataset will be fed into the YOLO model for training to get the well-trained detection model. The bounding box data extracted from each video frame by the detector serves as the initial state for the Kalman filter, subsequently enhancing the processing speed through the utilization of Python Multiprocessing. The results obtained by this tracker are used as a benchmark to compare with the results obtained by other two different trackers. The first one uses a Particle filter instead of a Kalman filter for tracking. The second one uses a tracker based on the Kalman filter and the Hungarian algorithm for tracking. The results show that the operating speed increase more than three times after adding the Multiprocessing in Python. The fastest method of the three is the Kalman filter with Multiprocessing in Python. © 2024 IEEE.

关键词： Kalman filters

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CTFU-Net:CNN-Transformer Fusion U-shaped Network for Moving object detection 3

CTFU-Net:CNN-Transformer Fusion U-shaped Network for Moving ...

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3rd international conference on image processing and Media Computing (ICIPMC)

作者： Xia, Tingting Yang, Yizhong Hefei Univ Technol Sch Microelect Hefei Peoples R China

ISBN: (纸本)9798350386660;9798350386677

The technique of extracting changing areas or moving objects from image sequences is known as moving object detection. The two most popular deep learning frameworks available now are convolutional neural network (CNN) and Transformer. CNN's strong local feature extraction abilities enable it to effectively capture local semantic information, while Transformers excel in modeling long-range semantic information. Consequently, we propose an innovative method for detecting moving objects by leveraging the benefits of both CNN and Transformer: CNN-Transformer fusion U-shaped network (CTFUNet). The main concept is to achieve information sharing between two different types of branches by hierarchically fusing the local feature information and the global context information through the feature interaction module (FIM). Furthermore, we also present a hybrid loss function to alleviate the foreground and background class imbalance problem. The experimental results demonstrate that CTFU-Net can extract more complete and clearer moving objects with better visual performance compared to the state-of-the-art methods.

关键词： moving object detection Transformer multi-head self-attention feature fusion hybrid loss

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Ball detection Algorithms Enhancement in Sport Robots 13

Ball Detection Algorithms Enhancement in Sport Robots

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13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Esfandiarpour, Mohammad Mirshabani, Seyed Mani Miandoab, Ehsan Maani Univ Tehran Dept Engn Sci Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

This paper presents a comparative analysis of four different ball detection algorithms that can be utilized in a wide range of sports robots. The study compares the performance of these algorithms in terms of accuracy, robustness, noise sensitivity, and computational time consumption. Studied algorithms are Color detection, Hough Circle, Frame differencing, and YOLOv8. Additionally, to overcome the algorithms' drawbacks, two different combinations of algorithms are proposed. The first approach is a combination of color detection and Hough circle, and the other one is a combination of color detection and YOLO. Then, these two hybrid methods are compared to previous algorithms and it is revealed that proposed algorithms are more accurate and efficient than well-known object detection algorithms. Employing these algorithms will lead to more accuracy in sports robots.

关键词： Ball detection Yolo circle detection Hough circle Color detection frame differencing

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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 ...

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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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