Coating defect detection is a critical aspect of ensuring product quality in the manufacturing process. However, due to the variety of coating defects and the complex detection background in actual production, detecting these defects can be challenging. To improve the accuracy and robustness of coating defect detection, a coating defect detection method based on data augmentation and network optimization design is proposed. First, a feature image random adaptive weighted mapping (FIRAWM) strategy is proposed, considering the prior accuracy, quantity, and context information of each category. Then, several improvements are made to the YOLOv5 network. Specifically, to mitigate the aliasing effects and enhance feature richness during the feature fusion process, an additional detection layer is added, and the coordinate attention module and the adaptively spatial feature fusion (ASFF) module are introduced. Finally, ablation and comparison experiments are performed to demonstrate the effectiveness of the proposed method. The results show that the method achieves 96.7 mAP50 with a processing speed of 61 FPS on the coating defect dataset, outperforming other popular detectors. Furthermore, the method is versatile and can be applied to detection tasks in various scenarios.