To enhance the accuracy and efficiency of defect detection in transmission line towers by reducing interference from noise signals and device performance,a defect detection method utilizing generative adversarial networks(GAN)and an improved region proposal network(RPN)was proposed.GAN was employed to capture significant images of transmission line towers,while a semi-soft thresholding function model was utilized to remove noise from the images and mitigate its impact on the defect detection process.The contour features of transmission line tower images were extracted using a random forest decision tree,and an enhanced RPN based on a multiscale algorithm was introduced.By inputting these features into the improved RPN model,defect localization and segmentation were performed for accurate defect detection in transmission line towers.Experimental results demonstrate the high accuracy,effectiveness,and efficiency of the proposed approach,thereby contributing to better quality control and a reduced occurrence of power accidents in transmission line towers.
关键词
生成式对抗网络/改进区域建议网络/输电线路/显著性图像/半软阈值函数模型/随机森林决策树
Key words
generative adversarial network/improved region proposal network/transmission line/significant images/semi-soft threshold function model/random forest decision tree
维普
万方数据