Image processing-based noise-resilient insulator defect detection using YOLOv8x

Accurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real-time operation due to the slow processing of the detection scheme. This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. A Gaussian blur and Laplacian sharpening-based hybrid scheme is proposed to mitigate the impacts of noises in insulator images. Experimental results indicate that the proposed framework can achieve a mean average precision (mAP) of 98.4% on noise-free images, surpassing benchmark models, such as YOLOv5x and YOLOv7 by 2.1% and 3.9%, respectively. Also, while the performance of a conventional system can decrease to a mAP of 93.3% in the worst case, the implementation of the proposed mitigation scheme ensures a mAP of 96.7% for that case. With an inference speed of 56.9 ms per image, this approach offers a promising solution for real-time power line inspection, contributing to enhanced power grid maintenance and safety.