YOLOv11-CoordAttention轻量化烟叶目标检测模型OA

To enhance the performance of the YOLOv11 model in intelligent grading tasks for tobacco leaf object detection and to ad-dress the issues of accuracy and timeliness in tobacco leaf object detection within resource-constrained environments,a lightweight YOLOv11-CoordAttention tobacco leaf object detection model was proposed.The effectiveness of various components was evaluated by comparing the impact of different backbone networks,convolutional modules,and attention mechanisms on model accuracy and speed.Ablation experiments were set up on this basis to investigate the practical effects of optimized combinations,thereby comprehensively revealing the model's performance in practical applications.The results indicated that the YOLOv11-Coord Attention model demon-strated superior comprehensive performance in the tobacco leaf object detection task,achieving a precision of 100%,recall of 99.4%,F1-score of 99.7%,mAP50 of 99.5%,with a model size of 5.2 MB,2.3×106 parameters,6.3×109 FLOPs,and a frame rate of 198.2 f/s.Compared to the YOLOv11 model,the YOLOv11-CoordAttention model improved precision by 1.2 percentage points and mean aver-age precision by 0.1 percentage points.The training process of the YOLOv11-CoordAttention model was stable,effective,and exhibit-ed outstanding performance.The losses for both the training and validation sets steadily decreased and converged as the training ep-ochs increased,indicating a sufficient learning process without overfitting.In terms of performance metrics,the model maintained high precision and recall,achieving high accuracy and low missed detection rates.Its mAP50 and mAP50-95 metrics were both excel-lent,indicating powerful detection capability and high robustness.The YOLOv11-CoordAttention model combined the advantages of being lightweight,efficient,and accurate.It could run stably on resource-constrained devices and was competent for tobacco leaf de-tection tasks in complex scenarios.