Objective Surface defect detection of aluminum alloy castings is a critical application in industry. Accurate and rapid detection of defects on the surface of castings can significantly improve production and quality. In response to challenges such as small defect targets easily confused defect categories and imprecise localization in images an improved SAD-YOLOv5 model based on a primary detector was proposed. Methods Addressing the problem of information loss during network training caused by strided convolutions and pooling layers in general convolutional neural networks space-to-depth SPD module was introduced to avoid the loss of fine-grained information and enhance the feature learning capability for small targets. To further improve the model accuracy adaptively spatial feature fusion ASFF and Decoupled Head were introduced in the network?? s Head. ASFF achieved adaptive fusion between different features suppressing inconsistencies among features of different scales to retain more discriminative information and enhance network learning capability. Decoupled Head replaced the original coupled head to decouple the classification and regression allowing classification to focus more on texture information and regression to focus more on edge information. This division of responsibilities further enhances the network?? s decision-making capability. Results Testing on a self- captured dataset for casting defect detection showed that SAD-YOLOv5 achieved mAP @ 0. 5 and mAP @ 0. 5 0. 95 of 95. 1% and 68% respectively. This represented a 1% and 3. 3% improvement over the baseline model YOLOv5 . Conclusion SAD-YOLOv5 demonstrates the ability to more accurately perform surface defect detection tasks on aluminum alloy castings.