Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN

doi:10.11930/j.issn.1004-9649.202005160

Electric Power ›› 2021, Vol. 54 ›› Issue (3): 45-54.DOI: 10.11930/j.issn.1004-9649.202005160

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Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN

ZHAO Zhenbing¹, ZHANG Shuai¹, JIANG Wei², WU Peng^3,4

1. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
2. State Grid Corporation of China, Beijing 100031, China;
3. Global Energy Interconnection Research Institute Co., Ltd., Beijing 102209, China;
4. Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory(GEIRI), Beijing 102209, China

Received:2020-05-21 Revised:2020-10-23 Online:2021-03-05 Published:2021-03-17
Supported by:
This work is supported by the National Natural Science Foundation of China (Research on Detection Method of Transmission Line Bolt Surface State Based on Deep Visual Knowledge Expression, No.61871182), Beijing Natural Science Foundation (Research on Deep Visual Perception and Cognitive Models for Mass Bolt Surface Defect Detection, No.4192055), Hebei Provincial Natural Science Foundation (Research on Micro-vision Defect Detection Model of Transmission Line Bolts Based on Regional Analysis, No.F2020502009)

Abstract

Abstract: Bolts are the mostly used fasteners on transmission lines, and their defect detection is an important content for transmission line inspection. As the bolt with missing pins are small targets, their positioning is difficult and their features are hard to extract. Aim at this problem, a detection method for bolts with missing pins is proposed based on the DBSCAN algorithm and FPN model. Firstly, the FPN model is used to locate the target area of the bolts with missing pins, and the areas with same morphological structure are clustered based on the DBSCAN clustering algorithm. Then, the FPN model is improved: based on the prior knowledge of bolts, the convolutional network is used to achieve bottom-up feature extraction, the bilinear interpolation method is used to transfer the high-level semantic information of the features to each level from top to bottom, the convolution filtering method is used to laterally strengthen the information fusion of high-level semantic features and high-resolution features, and a more optimized feature expression of bolts with missing pins is obtained. The improved FPN model is used to realize the preliminary detection of the bolts with missing pins. Finally, the DBSCAN clustering algorithm is adopted to screen the preliminary detection results for error detection, thereby achieving the accurate detection of the bolts with missing pins. Experimental results show that the detection accuracy of DBSCAN-FPN reaches up to 76.23% on the self-built data set, with the detection effect better than FPN, R-FCN and Faster R-CNN. The proposed method can effectively improve the detection accuracy of bolts with missing pins, which has practical significance for the operation and maintenance of transmission lines.

Key words: bolt, pin missing detection, DBSCAN algorithm, FPN, prior knowledge

ZHAO Zhenbing, ZHANG Shuai, JIANG Wei, WU Peng. Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN[J]. Electric Power, 2021, 54(3): 45-54.

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Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN

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