Search like an eagle: A cascaded model for insulator missing faults detection in aerial images

Abstract

Insulator missing fault is a serious accident of high-voltage transmission lines, which can cause abnormal energy supply. Recently, a lot of vision-based methods are proposed for detecting an insulator missing fault in aerial images. However, these methods usually lack efficiency and robustness due to the effect of the complex background interferences in the aerial images. More importantly, most of these methods cannot address the insulator multi-fault detection. This paper proposes an unprecedented cascaded model to detect insulator multi-fault in the aerial images to solve the existing challenges. Firstly, a total of 764 images are adopted to create a novel insulator missing faults dataset ‘IMF-detection’. Secondly, a new network is proposed to locate the insulator string from the complex background. Then, the located region that contains the insulator string is set to be an RoI (region of interest) region. Finally, the YOLO-v3 tiny network is trained and then used to detect the insulator missing faults in the RoI region. Experimental results and analysis validate that the proposed method is more efficient and robust than some previous works. Most importantly, the average running time of the proposed method is about 30ms, which demonstrates that it has the potential to be adopted for the on-line detection of insulator missing faults.