In order to improve the difficulty of locating small-scale components such as contact network pipe cap in the process of fault detection, a contact network pipe cap target location algorithm based on improved Faster R-CNN is proposed. The proportion and area of anchor boxes generated in the region proposal network (RPN) layer are improved by K-means clustering algorithm (K-means), the proposed algorithm has good performance in locating small components such as contact network pipe caps. The optimal feature extraction network is selected by comparing the accuracy, recall, accuracy, harmonic average of accuracy and recall F₁, and single sheet detection time of VGG16, resnet50, resnet101, and resnet152 feature extraction networks on the original and improved Faster R-CNN. The experimental results show that the improved Faster R-CNN deep network model based on resnet50 has obvious advantages in contact network pipe cap locating, the recall rate is 89.78%, the locating accuracy can reach 83.16%, the F₁ value is 86.34%, and the single detection time is 0.283 s.