Objective Aiming at the problems that there are many types of surface defects on aluminum profiles and significant differences among these defects which may easily lead to missed detections and false detections an improved YOLOv8s detection algorithm is proposed. Methods First the dilation-wise residual DWR module was introduced. Based on this the Bottleneck structure in the C2f of the backbone network was reconstructed to enhance the network?? s ability to extract complex features and enable the network to obtain context information more efficiently. Second an additional layer for detecting small targets was integrated into the existing neck network which aimed to extract and transmit the small target feature information that was more critical and discriminative in small-sized defects. The shallow and deep features were fused to reduce the missed detection rate and false detection rate and improve the detection accuracy. Finally the inner generalized intersection over union Inner-GIoU loss function was introduced to focus on the overlapping part inside the bounding box accelerating the model?? s convergence speed while improving the accuracy of bounding box regression. Results The improved algorithm was applied to the Tianchi aluminum profile dataset. The experimental results showed that the performance indicators of the improved algorithm in terms of precision recall and mAP@ 0. 5 reached 88. 7% 83. 4% and 88. 5% respectively. Compared with the original YOLOv8s algorithm these values were increased by 4. 1% 2. 1% and 2. 9% respectively. The improved algorithm can effectively identify different types of defects on the surface of aluminum profiles. Conclusion The effectiveness of the improved algorithm is verified by experiments. This improved algorithm solves the problem of poor detection results for some aluminum profile defects reduces missed and false detections and meets the current requirements of factories for detecting surface defects on aluminum profiles.