Lightweight improved transmission line insulator defect detection algorithm of YOLOv8

Heyan Huang; Hongyi Liu; Jue He; Liwei Wang; Cheng Jiang

doi:10.1117/12.3035866

16 August 2024 Lightweight improved transmission line insulator defect detection algorithm of YOLOv8

Heyan Huang, Hongyi Liu, Jue He, Liwei Wang, Cheng Jiang

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132301G (2024) https://doi.org/10.1117/12.3035866
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

The use of drones for insulator defect inspection of transmission lines has become the mainstream in the industry. In response to the problems of low detection speed, insufficient detection accuracy, high network complexity, and difficulty in deploying to mobile devices such as drones for insulator defects in power lines, propose a lightweight improved power transmission lines insulator defects algorithm of YOLOv8. Firstly, replace the backbone network of YOLOv8 with a lightweight MobileNetv3 network to reduce the number of parameters. Efficient Multi-Scale Attention (EMA) is used in MobileNetv3 to more accurately locate and identify objects. Secondly, Ghost Shuffle convolution (GSConv) is introduced to redesign the feature fusion network to ensure detection accuracy while reducing computing consumption. Finally, using the MPDIoU loss function to improve training convergence speed and make prediction box results more accurate. Experimental results can prove that the lightweight improved model achieves an accuracy of 97. 1% and a recall rate of 97. 3%, with a 70% reduction in the number of model parameters. It is more suitable for deployment on drone platforms and meets the requirements real-time and accuracy requirements of insulator defects detection on the edge side of transmission lines.

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

Citation Download Citation

Heyan Huang, Hongyi Liu, Jue He, Liwei Wang, and Cheng Jiang "Lightweight improved transmission line insulator defect detection algorithm of YOLOv8", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132301G (16 August 2024); https://doi.org/10.1117/12.3035866

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KEYWORDS

Object detection

Convolution

Detection and tracking algorithms

Defect detection

Feature extraction

Education and training

Feature fusion

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