DP-YOLOv5: Computer Vision-Based Risk Behavior Detection in Power Grids

Wang, Zhe; Zheng, Yubo; Li, Xinhang; Jiang, Xikang; Yuan, Zheng; Li, Lei; Zhang, Lin

doi:10.1007/978-3-030-88004-0_26

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…Yolo-V3 [30] solved this issue by using prepend boxes with variable sizes and enhanced detection action by adopting focal loss. Yolo-V4 [31] and Yolo-V5 [32], which followed in 2020 and 2021, are even more significant in terms of detection performance.…”

Section: Recognition Technologymentioning

confidence: 97%

A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

Cui,

Tian,

Cheng

2023

Computers, Materials &Amp; Continua

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Intelligent Space (IS) is widely regarded as a promising paradigm for improving quality of life through using service task processing. As the field matures, various state-of-the-art IS architectures have been proposed. Most of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service tasks. To this end, we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture (SOA). Specifically, we first use the distributed deployment of functional modules to solve the problem of high computing resource occupancy. Then, the Socket communication interface layer is designed to improve the calling efficiency of the function module. Next, the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing tasks. Finally, we design and deploy an interactive system based on Browser/Server (B/S) architecture, which aims to display the status of the robot in real-time as well as to expand and call the robot service. This system is integrated into the private cloud framework, which provides a feasible solution for improving the quality of life. Besides, it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right way. The results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service tasks. It is an efficient way to transmit data and reduce the computational burden on the robot. By using our cloud detection module, the robot system can save approximately 25% of the average CPU usage and reduce the average detection time by 0.1 s compared to the locally deployed system, demonstrating the reliability and practicality of our proposed architecture.

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Section: Recognition Technologymentioning

confidence: 97%

A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

Cui,

Tian,

Cheng

2023

Computers, Materials &Amp; Continua

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“…YOLOv5 [11], which was released in 2020, is a regression-based target identification algorithm that comes in four versions: YOLOv5s, YOLOv5m, YOLOv51, and YOLOv5x. The network with the least depth and feature map width is YOLOv5s.…”

Section: Yolov5mentioning

confidence: 99%

“…The output layer collects the target's position and category information instantly after putting the target to gets detected. The YOLO series [7][8][9][10][11] and the single shot multibox detector (SSD) series [12,13] are two examples of representative algorithms. Moreover, Bera et al [14] carried out a detailed performance and analysis of four CNN models: 1D CNN, 2D CNN, 3D CNN, and feature fusion based on CNN (FFCNN).…”

Section: Introductionmentioning

confidence: 99%

RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

Zhang¹,

Deng²,

Chen³

2023

Computers, Materials &Amp; Continua

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In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and better retain important feature information.(2) The feature information is enriched by improving the multi-scale feature pyramid structure and strengthening the feature fusion at different scales. (3) A new loss function is created and the diagonal penalty term of the anchor frame is introduced to improve the speed of training and the accuracy of reasoning. The proposed model is called residual feature fusion triple attention YOLO (RT-YOLO). Experiments showed that the mean average precision (mAP) of RT-YOLO is increased from 57.2% to 60.8% on the vehicle detection in aerial image (VEDAI) dataset, and the mAP is also increased by 1.7% on the remote sensing object detection (RSOD) dataset. The results show that the RT-YOLO outperforms other mainstream models in UAV aerial image object detection.

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“…31,32 However, it performs poorly with a small range of information and identifies objects with poor positioning accuracy and low recall rates. 33 Additionally, a deep CNN, which may have dozens or hundreds of layers, yields numerous weight parameters. This type of large network requires high computing resources and device storage requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it can achieve satisfactory performances for small target detection because of short-cut connections 31 , 32 . However, it performs poorly with a small range of information and identifies objects with poor positioning accuracy and low recall rates 33 . Additionally, a deep CNN, which may have dozens or hundreds of layers, yields numerous weight parameters.…”

Section: Introductionmentioning

confidence: 99%

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Zhang,

2024

J. Appl. Rem. Sens.

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Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges. Therefore, a combinational and sparse you-only-look-once (ComS-YOLO) neural network is put forward to accurately and quickly detect vehicles in aerial thermal infrared images. Therein, we adjust the structure of the deep neural network to balance the detection accuracy and running time. In addition, we propose an objective function that utilizes the diagonal distance of the corresponding minimum external rectangle, which prevents non-convergence when there is an inclusion relationship between the prediction and true boxes or in the case of width and height alignment. Furthermore, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, experimental results on the NWPU VHR-10 and DARPA VIVID datasets show that the proposed ComS-YOLO network effectively and efficiently identifies the vehicles with a low missed rate and false detection rate. Compared with the Faster R-CNN and a series of YOLO neural networks, the proposed neural network presents satisfactory and competitive results in terms of the detection accuracy and running time. Furthermore, vehicle detection experiments under different environments are also carried out, which shows that our method can achieve an excellent and desired performance on detection accuracy and robustness of vehicle detection.

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DP-YOLOv5: Computer Vision-Based Risk Behavior Detection in Power Grids

Cited by 3 publications

References 10 publications

A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

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