A Study on Detection of Pollution Status of Porcelain Insulators for Power Transmission Based on Improved YOLOv8-seg

Porcelain insulators are crucial components of insulation in power transmission lines and are still widely used in online applications. As the operational time increases, dust accumulation on the insulator surface leads to the formation of polluted insulators, resulting in a decrease in the external insulation level and increasing the risk of flashover under wet conditions, leading to widespread power outages. Current non-contact detection methods suffer from low detection efficiency and poor accuracy, making it difficult to intuitively and effectively assess the pollution status of insulators. Therefore, this paper proposes a method for detecting the pollution status of porcelain insulators based on improved YOLOv8-seg. Firstly, the YOLOv8-seg entity segmentation model is improved by adding the CBAM attention mechanism to focus on key feature information and enhance detection accuracy. Secondly, the SPPF module is upgraded to the SPPFCSPC module to optimize multi-scale object detection accuracy. Thirdly, the standard convolution (Conv) module is replaced with the Alterable Kernel Convolution (AKConv) module to reduce computational complexity and network overhead. Furthermore, the insulator region is further segmented, and image processing and filtering techniques are employed to extract color features from the insulator surface. Finally, the color features of insulators with different degrees of pollution under varying humidity are analyzed, and a pollution status detection method for insulators is proposed. Experimental results demonstrate that the proposed method meets the requirements of practical inspection in terms of accuracy and speed, providing valuable insights for insulator pollution status detection work.