Research on Technology of Autonomous Inspection System for UAV Based on Improved Yolov4

Abstract

With the continuous expansion of the grid scale and the development of artificial intelligence, UAV inspection is gradually replacing manual inspections and has become a mainstream of transmission line inspections by its high efficiency, low cost and high safety, which has greatly improved the inspection efficiency and inspection accuracy of transmission lines. However, the existing UAV inspections mainly rely on manual methods, requiring staff to operate and perform a lot of interventions in the field. The operating specifications are not uniform, which brings a lot of difficulties to the subsequent intelligent identification. Therefore, this paper proposes an autonomous transmission line UAV inspection system based on the improved yolov4. First, a priori box is set through K-means clustering to enhance the size adaptability, and the improved yolov4 could identify the key structure of the transmission tower. Second, the system could move the PTZ to place the relevant structure in the center of the image to complete the collection of image information. The test results show that the system can collect relevant information of transmission line towers in a standardized manner, improve the accuracy of image collection, and expand the scope of application of autonomous drone inspections, and provide a new direction for subsequent intelligent inspections.