To solve the limited resources and long time of detection equipment in detecting surface damage of steel cables, this study applies advanced technology of deep learning and convolutional neural networks (CNNs) to surface damage detection of the cables. On this basis, it proposes a YOLO-based defect detection network model to integrate GhostNet into the backbone network, and a new feature extraction module (ShuffleC3) based on ShuffleNet and attention mechanism, and then prunes and improves the Head part. Experimental results show that compared with the baseline YOLOv5s, the average accuracy of the improved network is increased by 1.1%. In addition, the number of parameters and calculations are reduced by 43.4% and 31% respectively, and the model size is reduced by 42.3%. Thus, the proposed model can reduce the network computing cost and maintain higher identification accuracy, which better meets the requirements for surface damage detection of steel cable materials.