Intelligent defect detection for Power Patrol Inspection

Abstract

Abstract In this paper, the insulator string images captured by Using unmanned aerial vehicles (UAVs) in the power inspection are taken as the research object. The image processing and deep learning methods are used to label the images, and the self-explosion fault of the identified and segmented insulator strings is identified and located. In the process of semantic segmentation of insulator string images, the given image data set is enhanced, and images are exchanged with image processing algorithms. Based on SegNet, each pixel in the picture is classified, and then the self-encoding is used for reference, and the image is first encoded and then decoded. The coding layer before the model uses the VGG-16 network to extract smaller pictures, so as to meet the requirements of semantic segmentation, and the decoding layer after the model obtains the classification probability value of each point and classifies each point. The experimental results show that the SegNet algorithm can realize the recognition and segmentation of insulator string images under complex conditions with high accuracy. In order to realize the identification and location of the self-exploding fault position of the insulator string, the training model can be used to detect the position of the self-exploding insulator in the image. First, the data of target detection is preprocessed, and then the data set transformation method of rotation transformation and image mirroring is used; then the SSD model is selected to identify and locate the self-explosion position of the insulator. The SSD model combines the advantages of the Faster R-CNN model and the YOLO model. It runs faster and has higher detection accuracy, which meets practical applications. The experimental results show that the SSD algorithm is more targeted to the insulator data set, and can improve the speed and accuracy of insulator identification and positioning.