Insulator defect detection algorithm based on multi-scale feature fusion optimization

Haohao Zhang; Guangbin Huang; Li Yang

doi:10.1117/12.3011642

7 December 2023 Insulator defect detection algorithm based on multi-scale feature fusion optimization

Haohao Zhang, Guangbin Huang, Li Yang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410X (2023) https://doi.org/10.1117/12.3011642
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The detection of insulator defects in transmission lines presents greater difficulty and challenges compared to regular object detection tasks due to the small size of defects in the image and complex background. In this paper, we propose an insulator defect detection algorithm based on multi-scale feature fusion optimization to address the problem of extracting effective information from insulator defect images with complex backgrounds. The algorithm incorporates a multi-scale feature fusion optimization module into the CornerNet network, which enhances the network's ability to capture global contextual information and local detailed information of the insulator feature map. Experimental results demonstrate that the proposed algorithm achieves an average precision of 93.65% for insulator defect detection with an inference time of 32.15ms, providing high detection accuracy and inference speed. The proposed approach has certain reference value for insulator defect detection in transmission lines.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haohao Zhang, Guangbin Huang, and Li Yang "Insulator defect detection algorithm based on multi-scale feature fusion optimization", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410X (7 December 2023); https://doi.org/10.1117/12.3011642

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KEYWORDS

Object detection

Defect detection

Feature fusion

Convolution

Image fusion

Visual process modeling

Visualization

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