In order to improve the efficiency of small target detection of aluminum surface defects, an aluminum surface defect detection algorithm based on improved YOLOv7-tiny was proposed. Firstly, the SimAM (similarity-based attention module) attention mechanism was added to the network to make the network pay more attention to the useful information and suppress the interference of invalid samples in the image. Secondly, the sppcspc structure in the backbone network was improved to Ghostsppcspc, which reduced the parameter redundancy during model training, and GSconv was used in the detection layer instead of the ordinary convolution, which lightened the network and strengthened the feature fusion to improve the network detection accuracy, and finally, the NWD(normalized wasserstein distance) was introduced to replace the original CIOU(complete intersection over union) loss function, which improves the network's accuracy in detecting small targets. The improved algorithm was applied to the Tianchi aluminium dataset for validation, and the experimental results show that the model was able to identify different kinds of defects on the surface of aluminium profiles effectively, with a 10.1% increase in mAP, a 6.4% decrease in the number of parameters, and a 12.2% decrease in the amount of computation compared with the original YOLOv7-tiny algorithm. The proposed method achieves a lightweight network model and can meet the current requirements of defect detection in the production site of aluminium profile factories.