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Addressing issues such as susceptibility to background interference and variability in feature scales of fine-grained defects on metal surfaces, as well as the relatively poor versatility of the baseline model YOLOv8n, this study proposes a YOLO-ADS algorithm for metal surface defect detection. Firstly, a novel CSPNet with Average SPP-Fast Block (ASPPFCSPC) module is proposed to enhance the model's fusion and representation ability between local features and global background information. Secondly, the newly improved module C2f_SimDCNv2 is utilized to improve the ability of the model to extract multi-scale features. Finally, the Space-to-Depth (SPD) layer is introduced to prevent the loss of fine-grained information from small target features and reduce the redundancy between convolution operations. Experimental results demonstrate that the mean Average Precision (mAP) and Precision of the YOLO-ADS algorithm on the steel strip surface defect dataset NEU-DET reach 81.4% and 79.7%, which are severally increased by 3.5% and 6.1%, and the Frames Per Second (FPS) reaches 140.4. Meanwhile, the versatility and robustness of the model are verified on the industrial steel surface defect dataset GC10-DET, the industrial aluminum surface defect dataset APSPC and even the larger public benchmark dataset VOC2012, the mAP is respectively increased by 3.7%, 3.4% and 4.3%. Compared with the mainstream detection algorithms, YOLO-ADS algorithm is ahead of a certain advanced level in detection accuracy while maintaining a good real-time performance, which provides an efficient and feasible solution for the field of metal surface defect detection. [ABSTRACT FROM AUTHOR]