An improved YOLOv7-tiny detection algorithm was proposed to address the problems such as various types of surface defects in aluminum profiles, large differences in defect scales and missed detection of small target defects. The spatial pyramid pooling module was reconstructed by utilizing the residual structure, parameter-free attention mechanism (SimAM), activation function (FReLU) and clipping convolution to capture more detailed information and strengthen the multi-scale learning ability of the network. The optimized detection layer was used to obtain more small target features and location information, and improve the detection ability of network multi-scale defect. Partial convolution was introduced to replace the 3×3 convolution in the efficient layer aggregation network (ELAN), then the lightweight model was used to reduce the computing and training burden. Combined with the similarity of normalized Wasserstein distance (NWD) loss measurement, the network convergence was accelerated and the detection ability of small target defects was improved. Test was conducted on the Tianchi aluminium profile dataset, and the results showed that the improved YOLOv7-tiny algorithm achieved the accuracy, recall, mean average accuracy (mAP@0.5) and detection speed of 95.0%, 91.8%, 94.5% and 45 frames per second, respectively, when the confidence threshold was 0.25. Compared with the original algorithm, the mAP@0.5 of the improved algorithm was increased by 4.2 percentage point as a whole, the average accuracy (AP) of the dirty spot defect was increased by 13.1 percentage point; the detection results of the improved algorithm for low-resolution images and interfered images was better than of the original algorithm, which showed that the proposed method had better generalization and anti-interference ability.