The existing aluminum surface-defect detection algorithms yield low detection accuracy in practical tasks. Hence, this paper proposes an improved YOLOv8s aluminum profile surface-defect detection algorithm (CDA-YOLOv8). First, the 3×3 downsampling convolution in the network was improved using the context guided block (CG Block) module. This enhances the extraction of features from the global context of the target and aggregate local salient features and global features, thus improving the feature generalization ability. Second, the dilation-wise residual (DWR) module was introduced to improve the Bottleneck structure in C2f, thus improving the multiscale feature-extraction capability. Finally, to address the feature-information loss of microdefects on the surface of aluminum profiles, an ASFP2 detection layer was designed, which integrates the small-target detection layer and the scale sequence feature fusion (SSFF) module. The layer was integrated into the neck of YOLOv8s to extract and transfer more critical small-target feature information in small-sized defects, thereby enhancing the detection performance. Experimental results show that the CDA-YOLOv8 algorithm achieves 93.4%, 80.4%, and 88.1% for indicators of precision, recall, and mean average precision, respectively, which are 5.1 percentage points, 2.4 percentage points, and 4.4 percentage points higher than those of the original YOLOv8s algorithm. This algorithm significantly improves detection performance, particularly through its ability to detect microdefects.