Target recognition algorithm for glass insulators in large substations under similar color interference
CHEN Yun
ZHANG Ying
LI Duanjiao
LIU Jianming
Abstract：[Objective]In the monitoring system of large substations,the target recognition of glass insulators is an important step to ensure the safe operation of power equipment.However,due to the complexity of the environment and the limitation of image acquisition conditions,glass insulator images often have problems such as insufficient clarity and similar color interference,which leads to the difficulty of target recognition and directly affects the safety monitoring effect of substations.[Methods]To solve this problem,a target recognition algorithm was proposed for glass insulators in large substations under similar color interference.The original image was converted from RGB space to HSV space to address insufficient image sharpness and similar color interference.By fine decomposition of hue H,saturation SS,and brightness V components in HSV space,the feature difference was calculated to enhance the color performance and visual effect of the image,so as to effectively eliminate similar color interference.An adaptive threshold segmentation technique,combined with the color features of HSV space,was used to accurately segment the image,and the glass insulator target region and complex background were separated.A dual-scale classification convolutional neural network(CNN)was designed to realize high-precision target recognition of glass insulators under complex background through multi-scale feature extraction and classification.The network combined local details and global context information to further improve the robustness and accuracy of recognition.[Results]The experimental results show that the proposed algorithm has significant advantages in application.In terms of color enhancement,the feature difference calculation in HSV space significantly improves the color contrast and visual effect of the image and effectively eliminates similar color interference.In terms of image segmentation performance,the adaptive threshold segmentation technique can accurately separate the glass insulator target region and the complex background,and the segmentation accuracy reaches a high level.In the aspect of target recognition,the dual-scale classification CNN shows strong anti-interference ability under complex background,and the recognition accuracy of glass insulators is significantly higher than that of traditional methods.[Conclusion]The target recognition algorithm proposed in this study for glass insulators in large substations under similar color interference successfully solves the target recognition problems including insufficient image sharpness and similar color interference through the organic combination of color enhancement,adaptive threshold segmentation,and dual-scale classification CNN.The algorithm has excellent performance in color enhancement,segmentation performance,and anti-interference ability and can recognize glass insulator targets efficiently and accurately,which provides a reliable technical guarantee for the safety monitoring of large substations.
Keywords：similar color interferencelarge substationcomplex backgroundglass insulatortarget recognitionadaptive threshold segmentationcolor enhancementdual-scale classification convolutional neural network
Publication Date：2025-07-25
Online Publishing Date：2025-09-18(First online date of this platform, not the publication date of the document)
Pages：8( 478-485 )