A lightweight YOLO-SGLS model is proposed based on the YOLO11 algorithm to address the shortcomings of existing elevator wire rope surface damage detection methods, such as insufficient accuracy and excessive computational complexity. Firstly, StarNet is used to replace the backbone network of YOLO11, and the star operation is used to improve feature extraction and computational performance. Meanwhile, the LSKA module is integrated with SPPF to enhance the feature expression and perception of the model through deep convolution. In addition, the Ghost module is improved using DynamicConv to obtain the Ghost Dynamic Conv （GDC) module, which is combined with C3K2 to reduce computational burden. Finally, an LSCD detection head is designed to improve inference speed. The experiment uses the Cable Damage dataset, which is divided into training, validation, and testing sets. In a specific experimental environment, ablation experiments, generalization experiments, and comparative experiments are conducted. The experiment shows that the YOLO-SGLS model reduces GFLOPs and parameter count by 40% and 36% respectively compared to the original base network YOLO11, improves accuracy by 5.5%, and only decreases average accuracy and recall by 0.3% and 1.9%. In the generalization ability test, the YOLO-SGLS model correctly recognizes 77 images out of 100 new datasets. It has been proven that the lightweight, accuracy, and robustness of the algorithm meet the requirements of elevator wire rope damage detection in practical application scenarios, especially for embedded devices with limited resources.