基于YOLO11-SDP模型的饰面人造板表面缺陷检测研究OA

Surface-decorated wood-based panels serve as a core raw material in the furniture industry,and surface defect detection is a critical step in quality control.Current detection methods commonly adopt YOLO-based machine vision,yet their performance in terms of precision and recall leave room for improvement.In resource-constrained deployment scenarios,the balance between inference speed and detection accuracy remains unresolved.To enhance the overall performance of existing detection models and apply the algorithm to surface defect detection of decorated wood-based panels,this study proposed an improved YOLO11-SDP(You Only Look Once11-Surface Decorated Panels)detection model.The original detection model was improved via three complementary measures:performing Mosaic augmentation on the dataset,replacing the original Feature Pyramid Network(FPN)module with a BiFPN module,and introducing a scale weight factor into the loss function.Experimental results showed that the improved model achieved a precision of 98.0%and a recall of 91.3%.Compared with the original model,the precision and recall rate were increased by 6.8%and 6.1%,respectively.These three enhancements significantly improved the model·s capacity to detect small targets,yielding greater generalization and robustness.When balancing the performance gains against the increased computational cost,the scale weight factor was proved superior to BiFPN,followed by Mosaic augmentation.Furthermore,the detection speed satisfied real-time processing requirements.Consequently,the YOLO11-SDP model was well-suited for the online detection of common defects—including frosting mark,water mark,spots,and crack—in the manufacturing of surface decorated wood-based panels,thereby providing technical support for the high-quality development of home furnishing and building materials.