Defect detection of small cotter pins in electric power transmission system from UAV images using deep learning techniques.

The detection of defects on small cotter pins that are installed in electric power fittings is an essential part of the inspection task of overhead lines using Unmanned Aerial Vehicles (UAV). It is challenging to detect small defects from a large number of UAV images. In this paper, an efficient and high-performance defect detection model called DDNet is proposed to recognize defects from images of unmanned aerial vehicles. The attention mechanism was adopted in the improved detection model in order to enhance the representation learning of the image. Inspired by the human visual system, the RFB module is added to the FPN module, increasing the receptive field of the entire detection network, which is conducive to the detection of small objects. Then a dataset of cotter pins for model training and testing was introduced. The study demonstrates that the proposed DDNet increases the average precision from 82.0 to 90.1% and reduces the miss rate of defect detection from 14.5 to 7.4% in our dataset compared to the baseline RetinaNet model. We also compared existing frameworks for object detection and discussed other common ways to improve precision. The results showed that our optimized model in this paper improved detection performance, which subsequently proved the practicability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]