It is one of the most critical technologies for unmanned electric locomotives to detect the obstacles in front of their operation quickly and accurately, which is of great significance for the safe operation of electric locomotives. Aiming at the problems of current computer vision detection methods, such as error warning, low detection accuracy, and slow detection speed, an obstacle intelligent detection method for unmanned electric locomotives based on an improved YOLOv3 (YOLOv3-4L) algorithm is proposed. The obstacle image data set of the electric locomotive running area is constructed to provide a testing environment for various obstacle detection algorithms. In the network structure, the darknet-53 feature extraction network is simplified, and the four-scale detection structure is formed by adding the shallow layer detection scale to the detection layer, which can improve the detection speed and accuracy of the algorithm for obstacles in front of the locomotive. Distance intersection over union loss function and Focal loss function are adopted to redesign the loss function of the target detector to further improve the detection accuracy of the algorithm. Traditional computer vision techniques such as perspective transformation, sliding window, and least square cubic polynomial are used to detect the track lines. By finding the area where the track was located and extending a certain distance to the outside of the track, the dangerous area of electric locomotive running is obtained. The improved YOLOv3 algorithm is utilized to detect obstacles, and only the types and positions of obstacles coincident with dangerous areas are output. The experimental results show that the traditional computer vision techniques such as perspective transformation, sliding window, and least square cubic polynomial can detect not only straight track but also curved track, which makes up for the shortcomings of the Hough transforms in detecting curved tracks. Compared with the original YOLOv3 algorithm, the YOLOv3-4L algorithm improves the mean average precision by 5.1%, and the detection speed increases by 7 fps. YOLOv3-4L detection model has high detection accuracy and speed, which can meet the actual working conditions and provide technical reference for unmanned driving of electric locomotives in underground coal mines.