  With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering
such as the inspection of the electrical equipment from distance, the demands
of efficient object detection algorithms for abundant images acquired by UAV
have also been significantly increased in recent years. In this work, we study
the performance of the region-based CNN for the electrical equipment defect
detection by using the UAV images. In order to train the detection model, we
collect a UAV images dataset composes of four classes of electrical equipment
defects with thousands of annotated labels. Then, based on the region-based
faster R-CNN model, we present a multi-class defects detection model for
electrical equipment which is more efficient and accurate than traditional
single class detection methods. Technically, we have replaced the RoI pooling
layer with a similar operation in Tensorflow and promoted the mini-batch to 128
per image in the training procedure. These improvements have slightly increased
the speed of detection without any accuracy loss. Therefore, the modified
region-based CNN could simultaneously detect multi-class of defects of the
electrical devices in nearly real time. Experimental results on the real word
electrical equipment images demonstrate that the proposed method achieves
better performance than the traditional object detection algorithms in defect
detection.
