GSD-YOLO: an improved YOLOv8n for insulator defect detection

Yue Zhou; Zhong Cao; Zhaohui Chen

doi:10.1117/12.3048099

3 October 2024 GSD-YOLO: an improved YOLOv8n for insulator defect detection

Yue Zhou, Zhong Cao, Zhaohui Chen

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Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327220 (2024) https://doi.org/10.1117/12.3048099
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

In this paper, an improved model GSD-YOLO built upon YOLOv8n is proposed to improve the precision and efficiency in detecting insulator defects. The model introduces three key improvements, namely GSConv lightweight convolution, small target detection Layer, and dynamic detection head (DyHead), which significantly enhance the computational efficiency and detection performance of the model. Experimental results illustrate that the mean average precision (mAP) of the GSD-YOLO model is improved from 87.5% to 91.2% and the recall rate is improved from 81.9% to 84.5%, which significantly outperforms the existing YOLOv8n model while maintaining a low computational complexity. In addition, the model’s ability to detect small-target defects in intricate backgrounds is significantly improved, with a minimum 2% increase in the mean average precision, which effectively adapts to the demands of complex power system environments and better satisfies the practical requirements of power inspections.

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

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Yue Zhou, Zhong Cao, and Zhaohui Chen "GSD-YOLO: an improved YOLOv8n for insulator defect detection", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327220 (3 October 2024); https://doi.org/10.1117/12.3048099

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KEYWORDS

Target detection

Performance modeling

Defect detection

Head

Small targets

Convolution

Defect inspection

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