To address the issue of various types and large-scale differences of surface defects in aluminum profiles, as well as the tendency for small targets to be missed, we suggest an improved detection model for small defects on the surface of aluminum profiles based on YOLOv5s, called KCC-YOLOv5 model. First, the IoU(intersection over union)-K-means++ algorithm is used to cluster anchor frames in place of the K-means algorithm, aiming to obtain the anchor frames that best fit the surface defects of aluminum profiles and improve the quality of small target anchor frames. Second, a global attention module C3C2F is proposed and introduced into the backbone layer to enhance the semantic information and global perception of small targets while reducing the number of parameters. Finally, the neck nearest neighbor interpolation upsampling method is replaced by a lightweight upsampling operator CARAFE(content-aware reassembly of features), which fully retains the small target information of the upsampled feature map. The experimental results show that the mean average precision of the improved KCC-YOLOv5 model is 94.6%, which represents 2.8 percentage points improvement compared to YOLOv5s. Furthermore, the average precision for small targets, such as bubbles and spots, are increased by 5.2 and 12.4 percentage points, respectively. Overall, the KCC-YOLOv5 model significantly enhances the detection accuracy of small targets while maintaining a small improvement in the detection accuracy of large targets.