A NOVEL SAMPLE LABELLING CRITERION FOR PIN DEFECT DETECTION IN UAV IMAGE

Abstract. With the rapid development of UAV technology, defect detection based on UAV images has expanded from power components such as insulators and dampers to bolts and pins. Different from the defect detection of insulators or dampers, there are two main difficulties in pin defect detection: (1) It is very small for bolts and pins compared to the entire image, usually less than 1%, and there are not enough features for detection; (2) Only bolts on link fittings need to be fixed with pins, while bolts in other parts do not need, so it is difficult to judge whether there are pin defects on bolts only based on the absence of pins on the bolts. Aiming at the above problems, cascade object detection method is adopted for pin defect detection in this paper, and improves the detection accuracy by gradually narrowing ROI (region of interest). The main contribution of this paper is to formulate a novel sample labelling criterion for cascade pin defect detection method. Building a sample set according to this criterion can not only greatly reduce the workload, but also improve the object detection accuracy. In this paper, YOLOv4 is used to validate the proposed method. The result shows that compared with the existing sample set building methods, the proposed sample labelling criterion improves the accuracy from 85.2% to 92% and recall from 85.7% to 94.2%.