Aiming at the problem that the existing steel surface defect detection algorithms are difficult to balance resource consumption and detection accuracy, an improved lightweight steel surface defect detection algorithm based on YOLO11s (EEC-YOLO) was proposed. Firstly, the ADown downsampling module was used to alleviate the loss of fine-grained information and realize the model lightweight. Secondly, a novel enhanced pooling channel attention (EPCA) attention mechanism is proposed and embedded in the YOLO11s backbone network to strengthen multi-scale defect feature extraction from the frequency domain dimension and reduce the lack of feature information. Finally, an enhanced screening feature pyramid network (ES-FPN) was designed, and the feature fusion network of YOLO11s was reconstructed by using the enhanced local attention (ELA) mechanism and group normalization to optimize the feature selection and fusion effect, so as to improve the attention and detection ability of the model for small targets. On NEU-DET dataset, compared with YOLO11s, its mAP@0.5 is improved by 3% to 79.8%, the number of parameters is reduced by 45.1%, and the amount of calculation is reduced by 30.5%. On the GC10-DET dataset, mAP@0.5 is increased by 3.2% to 65.6%, the number of parameters is reduced by 45.0%, and the amount of calculation is reduced by 30.2%. The proposed algorithm achieves a good balance between detection accuracy, computational cost and efficiency, and provides strong support for the industrial landing of edge terminal devices.