基于改进YOLO11的木材端面识别模型设计OA

Natural wood end surfaces exhibit irregular textures and defect features,making end surface recognition and localization a challenging problem.To enhance detection accuracy while reducing model parameters and improving com-putational efficiency for mobile deployment,this study proposes an improved end-to-end deep learning model tailored for log detection by enhancing the YOLO11 architecture.Firstly,the PP-LCNet backbone is adopted to replace the original YOLO11 backbone,effectively reducing the number of parameters,expanding the receptive field,and improving large target detection precision.Secondly,a parameter-free attention mechanism,SimAM,is integrated into the neck network to adaptively emphasize critical features and suppress redundant information,thereby enhancing small target recognition capabilities.Finally,the normalized Wasserstein distance(NWD)loss function is introduced,which is more suitable for measuring similarity between extremely small targets,further improves the accuracy and precision of wood end sur-face identification.Experimental results demonstrate that the improved model achieves higher end surface recognition ac-curacy compared to the baseline model,the improved model improves 2.65%and 5.29%on the mAP@0.5 and mAP@0.95 metrics,and FLOPs are decreased by 15.15%.It has good application value in the field of log volume measurement.