To address the issues of large parameter counts, insufficient detection accuracy, and difficulty in deployment on embedded devices in existing strip steel surface defect detection algorithms, a lightweight detection algorithm named YOLO-AMM based on improved YOLOv11n is proposed. Firstly, the ADown module is introduced to solve the problem of tiny defect feature loss caused by traditional strided convolution downsampling, thereby improving accuracy and to reduce the number of parameters for enhanced real-time performance. Secondly, the C3k2_MDSB module is constructed to strengthen multi-scale feature extraction capability and computational efficiency. Finally, the mixed local channel attention （MLCA） attention mechanism is incorporated into the feature fusion network to improve the feature extraction ability for defects of different scales. Experimental results show that compared with YOLOv11n, the mAP50 of the YOLO-AMM algorithm is increased by 3.6%, the recall rate by 2.3%, and the precision by 2.9%. Meanwhile, the number of parameters is reduced by 18.0%, the computational load by 14.1%, and the model size by 16.5%. The proposed algorithm outperforms a variety of classic lightweight algorithms and similar improved algorithms and is suitable for deployment on resource-constrained embedded devices.