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检索条件"任意字段=8th IEEE International Enterprise Distributed Object Computing Conference"

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Multi-object Tracking based on improved YOLO 8

Multi-object Tracking based on improved YOLO

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Li, Zhanbo Mu, Yuhong Li, Tingting Su, Jie Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China Zhengzhou University School of Cyber Science and Engineering Zhengzhou China

ISBN: (纸本)9798350302455

To improve tracking by detection algorithm missed detection and IDswitch problems caused by the occlusion of the target and the change of light and darkness. We propose a multi-target tracking algorithm based on improved YOLOv5 and DeepSORT. the SE and SimAM attention are adapted to YOLO v5 to improve the detector's precision, and the FocalEIOU loss function replaces the original CIOU to further accelerate the regression rate of the bounding box and improve the localization accuracy of the detector for the target. SAConv is added to the YOLOv5 backbone network and combined with the C3 layer, C3-SAC is proposed to address the unsuccessful identification problem caused by target occlusion, the feature extraction model of DeepSORT is adjusted and DIOU is used for matching. the improved algorithm is evaluated for detection and tracking on datasets VOC07+12, MOT16 and MOT20 respectively, and the mAP of VOC grows by 8.57%, MOTA of MOT16 and MOT20 grow by 3.88% and 6.45% respectively, and FPS meets with real-time tracking requirements. the result proves the effectiveness of the improved algorithm. © 2023 ieee.

关键词： Target tracking

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Gated Fusion and Dual Attention Network for Road Extraction from Satellite Imagery 8

Gated Fusion and Dual Attention Network for Road Extraction ...

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Wang, Haijuan Bai, Lin Ji, Bo Shen, Jiaming Jin, Ying Ye, Fei Zhu, Chunlin School of Electronics and Control Engineering Chang'An University Xi'an China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd. Hangzhou China Xi'An Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9798350335231

Automatic recognition and extraction of roads from high-resolution satellite images is a crucial task in remote sensing and computer vision. With the continuous development of remote sensing technology, more ground object information is contained in images, making it more challenging to extract roads due to increased interference. this paper proposes a gated fusion and dual attention network with an encoder-decoder structure to address this problem. the full-stage gated fusion module in skip connection selectively fuses feature maps of different scales using the Gated Fusion (GFF) unit, which increases the receptive field of the network and improves the accuracy of road extraction. the context extraction and dual attention module introduce rich global information and simultaneously weights features from both spatial and channel dimensions. this improves the semantic segmentation problem caused by focusing only on local information in current road extraction models. Experimental results on two public datasets show that GFDANet can effectively extract roads in complex scenes. © 2023 ieee.

关键词： Roads and streets

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Ground target detection method on moving base based on improved YoloV5 8

Ground target detection method on moving base based on impro...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Dang, Yansheng Wu, Zhongjian Jin, Daizhong Southwest Institute of Technical Physics Sichuan Chengdu China

ISBN: (纸本)9798350302455

Due to the poor stability of aerial detection equipment, the small proportion of ground target pixels and the occlusion of high-rise objects, air-to-ground target detection has brought great challenges. In order to detect ground targets accurately, we propose a modified YOLOV5 model based on visual *** ConvMixer architecture based on the hybrid idea is added to the backbone network of YOLOv5 to obtain more context information. In addition, the GhostNetV2 module is used to improve the neck network of YoloV5 to reduce the number of model parameters and calculation. K-means++ clustering algorithm is used to select the initial anchor box which is as close as possible to the global optimal solution. Finally, Wise-IoU evaluation index is used to improve the original IoU measurement. the experimental results show that the AP value of the proposed method is 0. 797, which is 6. 7% higher than that of YOLOv5. On the basis that the number of parameters and amount of computation are less than the original model, the method effectively inhibits the ground complex background interference and improves the accuracy of air-to-ground target detection. © 2023 ieee.

关键词： object detection

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Ship Detection in Maritime Surveillance System Based on YOLOX 8

Ship Detection in Maritime Surveillance System Based on YOLO...

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Ransheng, Yang Ying, Hu Hongfei, Zhang School of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798350335231

In this paper, we study the problem of ship target detection in maritime video surveillance systems, with special attention to the detection accuracy and adaptability under bad weather conditions. To this end, we propose an improved ship target detection method, including the addition of additive attention mechanism, and through testing on ship data sets, we verify the effectiveness of the proposed method. To further evaluate the performance of the method in severe weather conditions, we also used a flexible data enhancement strategy to generate a diverse synthetic test datasets, including different conditions such as rain, fog, and low illumination. Based on this datasets, we constructed a test datasets specifically for small vessel target detection, in which we evaluated the proposed approach. Experimental results show that compared with other mainstream competing algorithms such as Faster R-CNN, YOLOv3, YOLOv4, and YOLOX, our method has superior performance in terms of detection accuracy, robustness, and efficiency. In addition, the proposed method proves its good adaptability in ship detection under harsh imaging conditions. therefore, the improved method proposed in this paper is expected to provide higher accuracy and reliability for ship target detection in Marine video surveillance systems. © 2023 ieee.

关键词： object detection

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Research on Natural Scene Rice Pest Detection Method Based on YOLOv5 8

Research on Natural Scene Rice Pest Detection Method Based o...

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Shi, Jianhui JiangXi University of Science and Technology Jiangxi China

ISBN: (纸本)9798350335231

Rice is the world's most important food crop, feeding more than half the world's population. Each year, more than a third of the yield and quality of the rice crop is lost to pests and diseases. Rice pests seriously affect the high yield and quality of rice, and rice pest monitoring and early warning is an important task in rice production. the core of rice pest monitoring and early warning and integrated control of vector insects is the rapid and accurate identification of rice pests. Due to the large scale differences of rice pests in natural environment and the interference of complex environment such as weather change, light change and leaf background color, it is difficult to extract effective features, which makes rice pest detection difficult. this study is based on the latest research results of detection theory of deep learning object detection, combined with the characteristics of rice pest images, using the image dataset of rice pests collected in real natural environment, based on YOLOv5s combined with coordinate attention mechanism to add a new target detection layer adapted to rice pests, and at the same time to achieve the lightweight model introduced Ghost Bottleneck lightweight deep Finally, a weighted bi-directional feature pyramid network (BiFPN) is used to further enhance the multi-scale feature fusion. the experimental results show that the SDC-YOLOv5s model proposed in this paper can achieve accurate and fast detection of rice pests. © 2023 ieee.

关键词： object detection

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Dynamic Template Update Mechanism for Visual object Tracking 8

Dynamic Template Update Mechanism for Visual Object Tracking

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Hu, YongQi Cai, ZhongWang Nie, ZiJun Yu, ZiZheng Cao, Ying Yin, Zhijian Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9798350302455

Given the requirements for robust target classification and accurate target state estimation in visual tracking, SiamFC++ proposes a set of practical guidelines for designing high-performance general-purpose trackers by considering the special nature of visual tracking problems. Inspired by dynamic modules, We propose an empirical method for integrating a dynamic module into the image input, which is concatenated with the template module after feature maps are extracted by the backbone network. Since the position and shape of the object can change significantly within a video sequence, the added dynamic module can better focus on the target region of the feature map to obtain better similarity maps. Extensive experiments and comparisons demonstrate that our simple and effective method achieves reliable results on the benchmarks of LaSOT, TrackingNet, and GOT-10K and provides a significant speed advantage in real-time. © 2023 ieee.

关键词： Computer vision

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SF-YOLO: RGB-T Fusion object Detection in UAV Scenes 8

SF-YOLO: RGB-T Fusion Object Detection in UAV Scenes

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Zhang, Shoulong Xie, Ting Wang, Yiqi Liu, Yingying Dou, Yuling Wang, Shengke Ocean University of China Department of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350335231

In recent years, unmanned aerial vehicles (UAV) have been widely used in the field of object detection due to their high flexibility, strong maneuverability, and ability to carry multiple types of cameras. With the development of RGB-thermal (RGB-T) perception technology and the increasing demand for military use, a network structure that fuses RGB and thermal images with low false alarm rate and high detection precision has become a research focus. When visible-light cameras are affected by lighting conditions (such as low light and foggy weather), thermal image information provides useful complementary information. However, how to effectively fuse RGB and thermal images is still a challenge. Previous works have involved some simple fusion strategies, such as merging them at the input or connecting multimodal data features inside the model, or applying attention to each data modality. these fusion strategies are direct but not sufficient. To address this issue, we propose a multimodal data fusion target detection network called SF-YOLO. We develop an attention interaction enhanced fusion module (AIEF) to fully utilize complementary information while reducing the impact of redundant information. In addition, to address the problem of weak feature extraction ability in the CNN network structure, we introduce the Swin-Transformer module, and for the problem of large-scale variations in target size in UAV aerial images, we design a shallow enhanced feature pyramid (SEFP). the SF-YOLO multimodal data object detection algorithm proposed in this paper is tested on public datasets and our own dataset through a series of experiments, and the results show the effectiveness of our proposed method. this architecture is expected to be applied in military reconnaissance and public safety fields. © 2023 ieee.

关键词： object detection

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Toward a Maturity Model for Digital Business Ecosystems from an IT perspective 25

Toward a Maturity Model for Digital Business Ecosystems from...

引用

25th ieee international enterprise distributed object computing conference (ieee EDOC)

作者： Ehrensperger, Robert Sauerwein, Clemens Breu, Ruth Univ Innsbruck Inst Comp Sci A-6020 Innsbruck Austria

ISBN: (纸本)9781665435796

the digital transformation affects longstanding business models and leads to competition not only between enterprises but also between supply chains and networks. the concept of digital business ecosystems (DBEs) allows enterprises to concentrate on network co-creation and co-evolution of bundled services and products across enterprise boundaries. this study investigates the status of existing DBEs and their maturity. We interviewed 28 experts, reviewed more than 7,108 publications, and derived a maturity model from the obtained results. this maturity model helps practitioners to identify opportunities for improvement in their collaboration within DBEs and help researchers to assess and compare the current maturity level of existing DBEs.

关键词： Digital business ecosystems Maturity models enterprise architecture management Expert interviews

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Towards Ontology-based Competence Modeling in enterprise Architecture 25

Towards Ontology-based Competence Modeling in Enterprise Arc...

引用

25th ieee international enterprise distributed object computing conference (ieee EDOC)

作者： Calhau, Rodrigo F. Azevedo, Carlos L. B. Almeida, Joao Paulo A. Fed Univ Espirito Santo UFES Ontol & Conceptual Modeling Res Grp NEMO Vitoria ES Brazil Fed Inst Espirito Santo IFES LEDS Serra Brazil

ISBN: (纸本)9781665435796

Organizations today can hardly thrive without continuous attention to human resources development. In this context, competence-based approaches have been receiving increasing attention, as the availability of qualified people with the right combination of competences establishes itself as a major success factor. this paper investigates the integration of competence-based approaches into enterprise Architecture modeling. We follow a principled approach to propose competence modeling representation strategies in enterprise Architecture. We first identify a key set of competence-related concepts, analyze them based on the Unified Foundational Ontology, and then propose well-founded representation patterns for competence modeling in the ArchiMate language. We discuss how these patterns can be embedded in competence-based practices for the enterprise.

关键词： Competences Competence Modeling enterprise Architecture

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Vehicle-Infrastructure Cooperative 3D Detection via Point Cloud Filtering 8

Vehicle-Infrastructure Cooperative 3D Detection via Point Cl...

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8th international conference on Image, Vision and computing, ICIVC 2023

作者： Yan, Wenchao Cao, Hua Chen, Jiazhong College of Software Huazhong University of Science and Technology WuHan China College of Computer Science and Technology Huazhong University of Science and Technology WuHan China

ISBN: (纸本)9798350335231

Restricted by the vehicle's field of view and location, there are problems such as a lack of global perspective and limited long-distance sensing capability in the process of autonomous driving, so the assistance of road-side information is needed. At present, vehicle-infrastructure cooperation methods mainly suffer from the problems of vehicle-infrastructure sensor heterogeneity, spatial and temporal matching of vehicle-infrastructure sensors, high transmission cost, and significant computation on the vehicle-side. this paper proposes an early fusion with the point cloud filtering method for 3D object detection to reduce the transmission cost and alleviate the computational effort at the vehicle-side, which divides the whole 3D object detection process into three stages: First, a 3D object detection performed at the road-side, and the output prediction boxes are used as the road-side point cloud filter boxes, then the road-side point cloud is filtered according to the filter boxes, and only the point clouds within a specific range of the filter boxes are retained and transmitted to the vehicle-side. Finally, the vehicle-side fuses the point clouds from the road-side for vehicle 3D object detection and outputs the final detection results. the experimental results show that compared with the benchmark of the DAIR-V2X dataset, the transmission cost of this method is reduced by 92.07%. the detection accuracy is improved by 1.37% and 12.88% on average compared with the early fusion and late fusion methods in the benchmark, maintaining high accuracy while significantly reducing the communication load and saving the consumption of computational resources on the vehicle-side. © 2023 ieee.

关键词： object detection

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