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This paper presents a deep learning-based method for defect detection and classification of power distribution lines using video analysis. The first stage is dataset preparation in which different videos are recorded by a CoroCam 6D2 camera from distribution lines and labeled based on the defect type and the severity level of the defect. Then, a process is followed in such a way that a limited number of frames are used for processing, and power devices are detected in each frame using Faster R-CNN. The detector performance is improved by increasing the training dataset and changing the training hyper-parameters. Next, an equipment tracking technique is applied through the video frames. In the following, a method based on the classification of power equipment is presented. In this method, by corona color thresholding in all frames and applying a median filter over time, the connected components representing the corona are identified. Then, the frame containing the closest component to the component in the median image is selected. In the selected frame, the area around the component is cut and given to AlexNet or GoogleNet to determine the equipment type and the severity level of defect is determined. Also, the defective equipment phase is determined based on the insulators detected in the video. 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 defects in distribution lines, even in videos containing several possible defective devices. [ABSTRACT FROM AUTHOR]