Substation equipment defect identification method based on drone inspection

Xiuxiao Guo; Zhenli Wang; Tonghui Gu; Wanguo Wang; Bin Zhang; Zhizhou Sun; Zhaoxin Jia

doi:10.1117/12.3014863

4 March 2024 Substation equipment defect identification method based on drone inspection

Xiuxiao Guo, Zhenli Wang, Tonghui Gu, Wanguo Wang, Bin Zhang, Zhizhou Sun, Zhaoxin Jia

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 1298124 (2024) https://doi.org/10.1117/12.3014863
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Due to the complex environmental background of the substation, the wide field of view of drone inspection, and the small proportion of the target in the image, it is difficult to extract the defect feature of the small target and the high false detection rate. How to improve the recognition rate of the defect of the small target equipment is an urgent problem to be solved at present. The multimodel cascaded module, which is used to locate device components before defect detection or defect classification, is introduced into the Cascade-Rcnn algorithm to improve the accuracy of defects of small target device components and achieve accurate location of device defects.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiuxiao Guo, Zhenli Wang, Tonghui Gu, Wanguo Wang, Bin Zhang, Zhizhou Sun, and Zhaoxin Jia "Substation equipment defect identification method based on drone inspection", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 1298124 (4 March 2024); https://doi.org/10.1117/12.3014863

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