This study proposes a method for detecting slipping of stockbridge dampers based on key point training and learning. First, an improved SSD model is used to identify and locate the stockbridge damper. Thereafter, the key points of the stockbridge damper are selected, the MobileNetV3 network is trained, and the input area is set by the upper positioning results of the stockbridge damper to realize the detection of key points. Finally, discrimination rules are formulated according to the characteristics of line images. For m(m≥2) stockbridge dampers, the geometric constraint relationship among the key points is used to realize determination. For a single stockbridge damper, the EPnP algorithm is used to estimate the multiangle pose of the camera. Moreover, the spatial coordinates of the nearest points are obtained from the relationship between the pose and the pixel coordinates of the key point of the damp to determine whether the distance between the nearest points and the stockbridge damper is within the threshold range. The experimental results show that the proposed method can effectively identify slip faults and provide new ideas for detecting defects in transmission lines.