Target detection method based on deep learning applied in integrity evaluation of explosion proof electrical equipment

Bohan Li; Weijing Liu; Yuchen Lin; Tao Lin

doi:10.1117/12.2647783

23 August 2022 Target detection method based on deep learning applied in integrity evaluation of explosion proof electrical equipment

Bohan Li, Weijing Liu, Yuchen Lin, Tao Lin

Author Affiliations +

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 1233022 (2022) https://doi.org/10.1117/12.2647783
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

There are many types of explosion-proof equipment, so the problems in use are not the same. In view of these situations, this project takes the explosion-proof electrical equipment as the research object, and develops a set of integrity evaluation system of explosion-proof electrical equipment. This paper presents a defect detection method applied in the integrity evaluation system of explosion-proof electrical equipment. The proposed method first designs a lightweight network capable of achieving high-definition image detection under the YOLOV4 detection framework. Then, according to the specific data set, a priori box corresponding to the data distribution is generated. Finally, the distance-based K-means clustering method is used to extract the anchor block. The STN-OCR detects and identifies the defects from the natural images, thus applying the defect detection method in the explosion-proof electrical integrity evaluation system. This method improves the traditional evaluation process of traditional explosion-proof electrical equipment, which is cumbersome and inefficient, and has a certain impact on the application in some other fields in the future.

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Bohan Li, Weijing Liu, Yuchen Lin, and Tao Lin "Target detection method based on deep learning applied in integrity evaluation of explosion proof electrical equipment", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 1233022 (23 August 2022); https://doi.org/10.1117/12.2647783

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KEYWORDS

Defect detection

Convolution

Target detection

Aerospace engineering

Neural networks

Detection and tracking algorithms

Data modeling

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