Defect detection method of aerial insulator image based on deep learning

Abstract

UAV patrol inspection is a new patrol inspection method to complete the maintenance of overhead transmission line by using unmanned aircraft for navigation and shooting and relying on the advantages of independent operation. Compared with the traditional aerial patrol, UAV patrol has the advantages of strong adaptability, high safety factor, low risk and low cost. However, there are image defects and other methods in the process of application. Therefore, in the face of this situation, it is necessary to design an image defect detection method for aviation insulators based on deep learning. Creating the insulator image acquisition and deep learning environment, determined the image segmentation detection threshold under deep learning, and located the insulator image defect detection fault. The Crop-MobileNet network detection model is established, and the CNN processing method is used to detect the insulator defects in aerial photography. The final test results show that under different deep learning convolution ranges, compared with the traditional scaling factor defect detection group, the deep learning image defect detection group has a higher recall rate. It shows that the defect detection effect of aerial images is relatively good, which has practical significance.