Area-based non-maximum suppression algorithm for multi-object fault detection
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RESEARCH ARTICLE
Area-based non-maximum suppression algorithm for multi-object fault detection Jieyin BAI (✉)1, Jie ZHU2, Rui ZHAO1, Fengqiang GU3, Jiao WANG3 1 Nanrui Group Co., Ltd., Beijing 100192, China 2 State Grid Beijing Electric Power Company, Beijing 100031, China 3 Beijing Kedong Electric Power Control System Co., Ltd., Beijing 100192, China
© Higher Education Press 2020
Abstract 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. Keywords fault detection, area-based non-maximum suppression (A-NMS), cropping detection
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Introduction
The conventional inspection of power transmission lines by patrol personnel has been gradually replaced by unmanned aerial vehicles (UAVs). However, viewing the numerous images provided by UAVs individually is a time-consuming and complex task. Therefore, examining how to use computer technology to perform automatic recognition has become a popular topic. During the early stage of the machine learning, Received September 24, 2019; accepted November 15, 2019 E-mail: [email protected]
traditional image recognition and machine learning were often used to locate and detect faults. Sun constructed a slope model based on the appearance model of the insulators [1]. Zhang extracted the H vector from the hue, saturation, and value color space to perform contour matching [2]. After deep learning was proposed by Hinton and Salakhutdinov in 2006 [3], convolutional neural network (CNN) [4–7] and object detection [8–14] algorithms have become increasingly powerful and effective. Using the images captured by UAVs, Wang et al. applied the multi-object detection algorithm to electrical components, achieving an accuracy of 92.7% [15]. In this study, we detect several types of common faults in power transmission lines using an object detection algorithm. However, there are two problems associated with this algorithm that must be solved. The first problem is that a single object has multiple labels, and the second problem is that the detection capability of small objects is low. To solve the first problem, the traditional nonmaximum suppr
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