At present,the surface damage detection of wire rope based on machine vision is mostly qualitative detection,and there is very little research on quantitative detection.The number of broken wires is an important criterion for wire rope scrapped.Therefore,a quantitative identification method for wire rope surface damage based on machine vision and residual network was proposed.The collected wire rope damage images were cropped in batches to remove background noise.The images in the training set were randomly cropped and flipped horizontally by used data enhancement technology to expand the size of the training set.Then,the images in the data set were normalized and standardized to improve the convergence speed of the model.Finally,the training set and the verification set were input into the residual network optimized by using the SGD algorithm for training,and the test set was used to verify the model after the training was completed.The experimental results show that after iterative training,the model’s quantitative identification accuracy rate of wire rope damage on the test set is 93.5%.