基于改进掩膜区域卷积神经网络的输电线路绝缘子自爆检测

doi:10.19595/j.cnki.1000-6753.tces.220783

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Abstract Transmission lines occupy a relatively large proportion in the power system, in order to ensure the safe and stable operation of the power system, it is necessary to regularly inspect the transmission lines, among them, insulators in the transmission line play the role of insulation and support, due to the long-term hanging and working outdoors, prone to self-explosion defects, resulting in short-circuit faults on the line, and even large-scale power outages. With the development of artificial intelligence, the use of unmanned aerial vehicles (UAV) for line inspection, and then based on deep learning target detection methods for insulator defect detection has become an intelligent inspection method with great development potential. Due to the different shooting angles of the UAV, the insulators of the lines obtained by the inspection are different, and the environment in which the insulators are located is different, which results in the occlusion phenomenon of some insulators, to overcome these problems, this paper proposes to make relevant improvements on the basis of the two-stage target detection algorithm Mask region-convolutional neural network (Mask R-CNN), to ensure the detection speed and improve the detection rate of defective insulators by the algorithm.
For the insulator defects belonging to the category of small target detection, the convolutional block attention module (CBAM) attention mechanism is introduced in the backbone feature extraction network, so that the network can focus on the defect contour and obtain more interesting high-semantic information in the process of extracting the semantic information of the defect part. Then, in order to improve the limitations of semantic information still possessed by the feature layer, the parallel "bottom-up" path and feature fusion module are added to the original feature fusion network to promote the flow of information and global feature fusion. Once more, with the help of Generalized Intersection over Union (GIoU) to accurately characterize the distance between targets, the positioning performance of the model can be effectively improved when the targets overlap. Conclusively, the part of the original algorithmic loss function is replaced with the Tversky Loss function to alleviate the effect of sample imbalance on model training.
Based on the defective insulator dataset obtained by the UAV operation class of a transmission and transportation inspection center of the State Grid, the training of the improved network is carried out, and the model training effect is obtained from the convergence of the loss curve, and the generalization ability of the model is also improved. By using the improved model for defect detection and comparing the visual positioning results, the proposed algorithm avoids the interference of the high likelihood structure around the target to a certain extent, which realizes the effective detection of the insulator defect part, and improves the impact of small targets and sample unevenness on the detection. Compared with the original algorithm, the AP_50:95 of the proposed algorithm is increased to 0.56, AP₅₀ to 0.79, and AP₇₅ to 0.72. Finally, the performance of the algorithm is comprehensively compared, and the P-R curves of the improved before and after models under the conditions of AP₅₀ and AP₇₅ are compared, and it can be obtained that the corresponding curves of the improved algorithm are on the outside of the original algorithm curve, which shows the effectiveness of the proposed method, and the performance is better than that of the original algorithm.

Key words： Insulator defect detection mask region-convolutional neural network (Mask R-CNN) convolutional block attention module (CBAM) feature fusion generalized intersection over union (GIoU) Tversky loss

Received: 10 May 2022

PACS:	TM755
	TP389

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	Gou Junnian
	Du Susu
	Liu Li

Cite this article:

Gou Junnian,Du Susu,Liu Li. Transmission Line Insulator Self-Explosion Detection Based on Improved Mask Region-Convolutional Neural Network[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 47-59.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220783 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I1/47

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