Area-based non-maximum suppression algorithm for multi-object fault detection

Unmanned aerial vehicle (UAV) photography has become the main power system inspection method; however, automated fault detection remains a major challenge. Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously. The object detection method involving deep learning provides a new method for fault detection. However, the traditional non-maximum suppression (NMS) algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers. In this study, we propose an area-based non-maximum suppression (A-NMS) algorithm to solve the problem of one object having multiple labels. The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects. Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58% and 91.23%, respectively, in case of the aerial image datasets and realize multi-object fault detection in aerial images.