Crude Oil Leakage Detection Based on DA‐SR Framework

Gong, Fengkui; Gao, Yating; Yuan, Xiao Ming; Liu, Xin; Li, Yunjing; Ji, Xiaofeng

doi:10.1002/adts.202200273

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The rise of deep learning over the past few years has led to the rapid development of computer vision technology, and target detection technology, a critical branch in the field of computer vision, has been applied to numerous engineering projects (e.g., wind turbine blades target defect detection, [13] crude oil leak detection, [14] and optical remote sensing image detection [15] ). The deep learning-based target detection method, which takes an endto-end training approach, is capable of adaptive feature extraction and better robustness.…”

Section: Detection Based On Deep Learningmentioning

confidence: 99%

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

Chu

Yang

2023

Advcd Theory and Sims

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A double branch fusion network is proposed based on unmanned aerial vehicle (UAV) inspection images to increase the detection accuracy of vital components and defects in transmission lines. The backbone feature extraction network comprises a combination of a convolutional neural network (CNN) and a Transformer network. To be specific, the CNN should extract local information, and the Transformer network is responsible for the extraction of global information. Besides, global information and local information have semantic differences, while resulting in feature aliasing after fusion. To solve this problem, a multiscale convolution module and a multiscale pooling module are proposed to solve semantic differences and feature aliasing through the interaction between two types of information. In general, the enhanced feature extraction network comprises a residual‐like convolution module, which can reduce the loss of detailed information (e.g., edge contours) and further extract high‐level semantic information from the deep network. Besides, it performs feature fusion in multiple regions in the enhanced feature extraction network, such that the multi‐scale adaptability of the neural network is effectively enhanced. Last, the fused feature information at different scales is decoded, and the final detection results are yielded.

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Section: Detection Based On Deep Learningmentioning

confidence: 99%

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

Chu

Yang

2023

Advcd Theory and Sims

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“…Ref. [33] proposed the generation of synthetic oil leakage images based on generative adversarial networks (CycleGAN), in which the original image was combined with the synthetic image and the YOLOv4 network was trained, and this method can be used for substation equipment oil leakage detection. The above-mentioned deep-learningbased approaches for oil leakage detection in substation equipment have achieved better performance in terms of accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN

Yang,

Ma,

Guo

et al. 2023

Sensors

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Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.

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“…[4,5] In recent years, some related researchers have made remarkable breakthroughs [6][7][8][9][10] by the development of deep learning. Currently, object detection has been widely used in the fields of autonomous driving, [11,12] defect detection, [13,14] robot vision, [15] video surveillance, [16,17] etc.…”

Section: Introductionmentioning

confidence: 99%

GPNet: Key Point Generation Auxiliary Network for Object Detection

Shao

Sun

Liu

et al. 2023

Advcd Theory and Sims

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For existing object detectors, anchor‐based detectors lack global information, while anchor‐free detectors based on key points lack prior position information. The above issues may lead to the imbalance of detection accuracy and shape robustness. In order to alleviate the above contradictions, a new auxiliary network key point generation network (GPNet) is proposed to improve the performance of existing object detectors. Specifically, a series of key points are generated through ground truth (GT) supervision to obtain more global information. These key points generate pseudo boxes (Pbox) with learnable parameters. Pbox has more specific prior information than the manually designed candidate boxes. The scale information of the Pbox is embed into the classification branch to obtain a more appropriate receptive field. In addition, a novel improved strategy for label assignment by combining Pbox and GT to enhance the ability to classify positive and negative samples is proposed. Extensive experiments on multiple dense prediction methods validate the effectiveness of GPNet, with a performance improvement of 1.5 AP over baseline. In particular, with ResNext‐101‐64× 4d‐DCN as the backbone, this method achieves 49.5 AP with single‐scale testing.

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Crude Oil Leakage Detection Based on DA‐SR Framework

Cited by 6 publications

References 38 publications

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN

GPNet: Key Point Generation Auxiliary Network for Object Detection

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