Research on Defect Detection Algorithm of Absorptive Coating Materials Based on Improved YOLOv11

The present study proposes an absorptive coating defect detection method based on an improved YOLOv11 model, with a view to addressing the issues of low detection accuracy, false positives, and missed detections in existing absorptive coating defect detection algorithms when handling small-sized targets. Specifically, given the limited availability of relevant datasets, this study collected and constructed a dataset of visible light and infrared images of invisible coating materials containing typical defects, and processed the data using a variety of advanced data augmentation methods. Subsequently, the model was enhanced with a global attention mechanism (GAM) to improve feature extraction and detection accuracy for small objects. Regarding the loss function, this study adopted the Wise-IoU loss function, which adjusts the model’s attention to different samples. Finally, comparative and ablation experiments were conducted on the self-built dataset. The experimental findings demonstrate that the improved YOLOv11 model attained a precision of 93%, accompanied by a recall of 88%. This signifies an augmentation of 3% and 10%, respectively, in comparison to the conventional YOLOv11 model. This serves to substantiate the model’s exceptional performance in the task of detecting defects in absorptive coating materials.