In this study, we propose a method based on improved Faster R-CNN with respect to the influence of light penetration, occlusion, environmental background, and shooting angle on the insulation piercing connectors and bolts on the transmission line. First, we enhanced the acquired datasets via flipping, panning, and angle rotation. Second, we compared the influences of different training sets on the model. Finally, we used a deep residual network (ResNet50) having a considerable network depth and less amount of computation to replace the VGG-16 (Visual Geometry Group 16) network for extracting the image features owing to the small size of the bolt. Further, we analyzed the influences of different models and parameters on the identification accuracy. The result proves that the improved Faster R-CNN model has an mAP value of 92.4%, which is 2.8 percentage higher than that of the unmodified Faster R-CNN model. The deep learning target detection model can be used to appropriately detect and identify the insulation piercing connectors as well as bolts having different resolutions and position angles. Therefore, this model has a high engineering application value.