The damage of the outer sheath of the cable at the industrial site mainly relies on manual inspection, which consumes manpower, is subject to high subjectivity, and is prone to blind spots. The realtime performance is poor and the manual inspection of some industrial sites is more dangerous. Aiming at a series of problems caused by manual inspection, this paper proposes an improved Faster RCNN cable sheath damage detection method. In order to improve the generalization ability of the model, grayscale, flip, pan, and sharpen the collected training set are used for data enhancement; use the feature extraction network RseNet50 with fewer parameters and deeper layers to replace the original VGG16 as the backbone feature extraction network; use migration learning to use the weights trained on the ImageNET dataset as the initial weights of the model; use bilinear interpolation to replace the ROI Pooling operation; use the Kmeans clustering algorithm to analyze the original data Cluster analysis was performed on the collection, the Silhouette method was used as the evaluation standard, and the anchor frame of the outer sheath damage detection was customized based on the clustering results. Experimental results show that the improved Faster RCNN has an average accuracy (mAP) of 8833% for the detection of damage to the outer sheath of the cable, which is 549% higher than the original Faster RCNN, and is better than the classic SSD algorithm and YOLOv3 algorithm. The improved detection speed achieve 036 frame/s to meet the testing requirements. This model can be subsequently equipped with various mobile detection platforms and has high engineering value.