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

共 1278 条记录，以下是31-40 订阅

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Drill Pipe Counting Method in Coal Mine Based on Improved object Tracking 8

Drill Pipe Counting Method in Coal Mine Based on Improved Ob...

引用

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

作者： Wu, Xiaojun Xie, Peiru Yuan, Sheng Hu, Qiao Wang, Xinyi She, Yue Jiaotong University Xi'an School of Software Engineering Xi'an China Jiaotong University School of Mechanical Engineering Xi'an Xi'an China

ISBN: (纸本)9798350302455

With the advancement of mining intelligence, the application of artificial intelligence in this field is increasing. However, how to accurately count the number of drill pipes and improve safety production is still a challenge. To address this issue, one algorithm is proposed that involves analyzing the drilling pattern and using visual object tracking to count drill pipes. the You Only Look Once (YOLO) is improved by using rotated object detection with the circular smoothing label to obtain the position and angle information of the drill in the first frame. A dynamic template update strategy is adopted to improve the accuracy of Transformer Tracking (TransT) in long-term object tracking. Finally, the drill pipe is counted by using the peak points of the trajectory and worker actions with PoseConv3D. Compared with the baseline, the proposed algorithm achieves an average accuracy of 95.62% and a counting efficiency of 42.5 frames per second (FPS), making it more robust and accurate in the field of drill pipe counting. © 2023 ieee.

关键词： Drills

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A Bolt Rust Detection System for Smart Construction Sites 8

A Bolt Rust Detection System for Smart Construction Sites

引用

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

作者： Sun, Yu Zheng, Linxiao Lu, Zijun Lian, Qiufang Zhou, Lin College of Information Science and Engineering Huaqiao University Xiamen China

ISBN: (纸本)9798350335231

the paper proposes a computer vision-based embedded intelligent construction site bolt rust detection system. Firstly, the limitations of manual and traditional algorithms in ensuring construction equipment safety are discussed, along with the advantages of embedded systems in this domain. Subsequently, the challenges related to acquiring construction site datasets and the real-time detection capability of existing deep object detection algorithms on resource-constrained edge devices are thoroughly addressed. To tackle these challenges, multiple dataset augmentation techniques are employed to expand the bolt rust dataset, and a comprehensive comparison of mainstream object detection algorithms is conducted. Ultimately, YOLOv7-tiny is selected as the detection model, and through training on a custom dataset, an average detection accuracy of 87.2% is achieved for the object detection network. In practical implementation, the full-precision model is deployed on the RK3588S platform with a fixed input size of 640×640. Experimental results demonstrate that the system is capable of real-time and accurate detection at a rate of 20 frames per second, meeting the requirements of real-time bolt rust detection on construction sites. © 2023 ieee.

关键词： object detection

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DCE: A High-performance, Scalable, enterprise-level Cloud Native Operating System 8

DCE: A High-performance, Scalable, Enterprise-level Cloud Na...

引用

8th ieee international conference on Smart Cloud, SmartCloud 2023

作者： Chen, Qiyan Zhang, Hongbing Yan, Kai Guo, Feng Yang, Hongjun Bai, Youjia Hou, Lingyu Shanghai DaoCloud Network Technology Co. Ltd. Shanghai China

ISBN: (纸本)9798350313505

the cloud native methodology has emerged as an optimized solution to meet the ever-evolving demands and challenges of modern application deployment and management. DaoCloud enterprise (DCE), a high-performance and scalable cloud native operating system, developed by Shanghai DaoCloud Network Technology Co., Ltd. has experienced rapid growth over the past eight years, gaining valuable experience in various fields, including finance, automotive, manufacturing, smart city and retail etc. the company has been dedicated to exploring cloud native technology, such efforts fruited as eight major components offered by DCE, including multi-cloud orchestration, microservice governance, container management, insight, global management, storage, service mesh, and workbench. DCE empowers the enterprises to embrace their digital future, and efficiently increase their business agility. ©2023 ieee.

关键词： Containers

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Research of Surface Floating object Detection Method Based on Improved YOLOv4 8

Research of Surface Floating Object Detection Method Based o...

引用

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

作者： Tang, Wei Xu, Dahai Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi'an China

ISBN: (纸本)9798350302455

this paper proposes an improved YOLOv4 water surface floating object detection method to address the problem that floating objects on the water surface are difficult to detect due to their small size and existing detection algorithms are computationally intensive and difficult to run in real time on embedded devices. the specific idea is to use the MobileNetV3 network to replace the backbone network in YOLOv4;at the same time, negative feedback is introduced, and the proportion of the loss of small-sized targets in the overall loss during the training process is used as feedback, so that selective data enhancement can be performed to improve the detection accuracy of small-sized targets. the experimental results show that compared with the traditional YOLOv4 algorithm, the model parameter number of the proposed algorithm is reduced by 82.4% and the detection speed is improved by 52%. © 2023 ieee.

关键词： object detection

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Bio-inspired Saliency computing Neural Network for UAV Small object Detection 8th

Bio-inspired Saliency Computing Neural Network for UAV Smal...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Hu, Pingge Zhang, Xiaoteng Cang, Yueyang Shi, Li Department of Automation Tsinghua University Beijing100084 China

ISBN: (纸本)9789819601875

the detection of small objects in dynamic, high-altitude scenarios is essential for many biological and mechanical systems, such as unmanned aerial vehicles (UAVs). the midbrain network of birds, including the Optic Tectum (OT) and associated nuclei, provides a biological blueprint for efficient visual saliency detection, which is crucial for this task. this paper introduces a novel Midbrain Recurrent Neural Network (Mb-RNN) model that mimics this avian system to improve small object detection in UAVs. Based on this description, we propose a novel mathematical description of the midbrain network and develop the Mb-RNN. the proposed Mb-RNN is embedded within an RNN cell to capture the dynamic interplay of saliency detection, global inhibition, and response amplification of the avian visual system. We further build a bio-inspired UAV small object detection system, OTNet+, which integrates Mb-RNN and other deep neural networks to improve detection performance. We demonstrate the superiority of OTNet+ over state-of-the-art approaches using UAV datasets, providing a promising direction for bio-inspired computer vision systems in practical applications. Our approach provides a deeper understanding of avian neural mechanisms. It illustrates how these biological visual mechanisms can be abstracted to improve applications in computer vision, particularly in UAV-based tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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A Joint Framework based on PU Learning and Active Learning for object Detection 8

A Joint Framework based on PU Learning and Active Learning f...

引用

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

作者： Li, Yue Jia, Minzheng North China University of Technology School of Information Science and Technology Beijing100144 China Beijing Polytechnic College Department of Information Engineering Beijing100042 China

ISBN: (纸本)9798350302455

the problem of object detection is a critical issue in the field of image processing, and the quality of labels can significantly affect the performance of detectors. In recent years, many deep learning frameworks have achieved good detection results. However, in many practical scenarios of object detection, collecting complete annotation is a time-consuming and costly process. therefore, how to use partially labeled samples to solve the object detection problem has attracted the attention of many researchers. We proposed a new joint training framework based on PU (Positive-Unlabeled) learning and active learning to solve the above problem, named the PU-AL learning method. this framework is based on the Faster RCNN two-stage detector, and we introduce the PU learning method to solve the problem of incomplete annotations, adapting the traditional semi-supervised learning problem to a PU learning problem. We also introduce the loss branch prediction method based on Active Learning to improve the selective labeling method of sample selection. Experiment results demonstrate that the proposed framework outperforms the baseline methods in varying degrees of missing annotation. © 2023 ieee.

关键词： object detection

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An Abnormal Behavior Recognition System of Equipment Delivery Based on Image Recognition Technology 8

An Abnormal Behavior Recognition System of Equipment Deliver...

引用

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

作者： Liu, Zhiwei Hou, Bin Ning, Ke Wang, Nan Liu, Xingting Wang, Haiqi Materials Branch of State Grid Shanxi Electric Power Company Taiyuan China State Grid Shanxi Electric Power Research Institute Taiyuan China

ISBN: (纸本)9798350302455

the high quality of power grid equipment is the basis for the safety of the power grid, and ensuring the quality of equipment is the core task of the material department. Before putting the equipment into operation, quality inspection is essential for the State Grid Corporation to ensure equipment quality. this paper proposes an abnormal behavior recognition system based on image recognition technology to guarantee the correctness and impartiality of the samples sent for testing. the system is composed of a human body object detection module, a camera occlusion detection module, and a camera monitoring angle adjustment alarm module. In the human body object detection module, a human body part detection network is added based on Faster R-CNN framework to obtain more representative features, which is used to improve the accuracy of the human body in the case of occlusion and to reduce the false negative rate. Testing results from the monitoring video show that the system can effectively identify abnormal behaviors during the equipment samples delivery process. © 2023 ieee.

关键词： object detection

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Correlation Filter Tracking Algorithm Based on Filter Space Constraint and object Re-Detection 8

Correlation Filter Tracking Algorithm Based on Filter Space ...

引用

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

作者： Tian, Yan Ren, Sixi Xu, Zhaohui Zhang, Yongkang Xi'An Institute of Optics and Precision Mechanics of CAS CAS Key Laboratory of Space Precision Measurement Technology Xi'an China

ISBN: (纸本)9798350335231

fast Discriminative Scale Space Tracking (fDSST) is an excellent object tracking algorithm with a good balance between tracking precision and running speed. In order to improve its long-term and stable tracking performance, especially its ability to recapture object, the fDSST_SrRed algorithm is proposed in this paper. the improvement details include: in order to reduce the negative impact of boundary effect on fDSST, a spatial constraint method of position filter is proposed;Aiming at the defect that the fDSST cannot relocate the object after the object is occluded and disappeared, an object re-detection method based on zero-filling of position filter was proposed. In order to judge when to start object detection, a tracking confidence evaluation index is proposed. Based on this confidence evaluation index, a dynamic filter update method is designed to make the filter learn more accurate object information and background information. Experiments show that compared with the baseline fDSST, proposed algorithm has a great improvement in the tracking precision of occluded and disappeared scenes. Compared with other correlation filter based long-term tracking algorithms (LCT, MUSTER and FuCoLoT), the proposed algorithm not only has good tracking precision, but also has the fastest running speed. the experimental results show that the improvement is effective. © 2023 ieee.

关键词： object detection

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Data Augmentation Method on Drone object Detection with YOLOv5 Algorithm 8

Data Augmentation Method on Drone Object Detection with YOLO...

引用

8th international conference on Informatics and computing, ICIC 2023

作者： Alin, Ariel Yonatan Kusrini Yuana, Kumara Ari Universitas AMIKOM Yogyakarta Magister of Informatics Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350342604

this study aims to evaluate and compare various types of data augmentation in drone object detection using the YOLOv5 algorithm. Data augmentation is an important technique in image data processing to increase the diversity and quality of training data, so as to improve the performance of object detection models. In this study, several different types of data augmentation were applied to the existing drone object detection dataset. this research method consists of fourteen types of data augmentation, namely Flip, Rotate, Crop, Rotation, Shear, Hue, Blur, Grayscale, Saturation, Brightness, Exposure, Noise, Cutout, and Mosaic. the use of the Mosaic method as the newest type of data augmentation is expected to provide better results in drone object detection. the test is carried out by measuring the Precision-Recall evaluation metrics for each type of data augmentation. the results showed that the Mosaic augmentation type gave the highest Precision-Recall value of 0.993 compared to other augmentation types. the use of Mosaic as a data augmentation in drone object detection with the YOLOv5 algorithm can significantly improve object detection performance. © 2023 ieee.

关键词： Drones

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A Hybrid Model for object Detection Based on Feature-Level Camera-Radar Fusion in Autonomous Driving 8

A Hybrid Model for Object Detection Based on Feature-Level C...

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

作者： Jin, Yuhao Zhu, Xiaohui Yue, Yong Ma, Jieming Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China

ISBN: (纸本)9798350302455

Preventing car collisions through object detection has always been a major research direction in the field of autonomous driving. Recent years, camera-based object detection technology has achieved great success. However, its performance is still insufficient under poor lighting or weather conditions. therefore, the fusion of various sensor information has become a new trend in object detection for autonomous driving. this paper proposes a hybrid object detection model that fuses millimeter-wave radar and camera at the feature level. the model uses a traditional convolutional neural network to extract features from data collected by the radar and camera, and performs multi-scale deep fusion. Subsequently, a multi-scale deformable attention module is used to process the fused feature maps for object detection. We tested this model on the nuScenes for autonomous driving, which includes night and rainy scenes. the hybrid model achieved a mean average precision (mAP) of 47.8%, which is 1.4% higher than that of the baseline object detection model. © 2023 ieee.

关键词： object detection

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