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As one of the non-contact methods for incipient fault diagnosis of power distribution lines, UV-Visible imaging has become popular due to its good performance and robustness against environmental parameters. This paper presents a deep learning-based method for UV-Visible video processing. First, for preparing the dataset, some videos are acquired from distribution lines using CoroCam 6D2 considering observation distance, the imager's gain, air pressure, and humidity as effective parameters on the discharge area in the images. Then, based on the logged information of unplanned power outages in the system and also planned inspections over a year, the incipient fault type and the severity level of the incipient fault are determined for each video. For processing each video, frames are extracted with the rate according to the nominal voltage of the line. Power devices are detected in each frame using Faster R-CNN and are tracked through the whole video frames to compensate the camera movement. Then, color thresholding is used for each device to identify corona discharges in the frames. In-depth median filtering is also used through the video to eliminate noises in the UV channel. Finally, based on the ratio of the detected discharge area to the area of the equipment, the severity level of the incipient fault is determined. The proposed method not only performs better than the state-of-the-art but also is a practical method, and with the least dependence on environmental conditions, can automatically identify incipient faults in distribution lines, even in videos containing several possible defective devices. [ABSTRACT FROM AUTHOR]