"Research on Nondestructive Foreign Matter Detection in Pu-erh ripe tea Based on Bionic Vision and Deep Learning"

"To address the challenges of small foreign matter size, severe occlusion, and complex backgrounds in the processing of Pu-erh ripe tea, this study drew on the visual mechanisms of primates and proposed an improved YOLOv13 based network, AE-YOLOv13-S. To mitigate the loss of fine details, the weakening of discriminative features, and the frequent occurrence of missed and false detections, the Adaptive Sparse Self Attention Network was introduced to optimize the backbone of the network, inspired by the sequential cognitive pattern of primates in target search, local verification, selective integration, and final decision making. To address insufficient long range semantic association and the submergence of fine grained differences in background noise, Emulating Self Attention with Convolution was employed to optimize part of the Conv modules of the network, drawing on the hierarchical information processing mechanism of primates from peripheral perception to central fine analysis. In response to the limitations that bounding boxes can only approximately enclose targets, the large amount of geometric supervision noise, the obvious localization deviation, and delayed model convergence, a Scale-based Dynamic Loss, inspired by primate visual perception mechanisms, was introduced to optimize the network's loss function. The results showed that, during training, compared with the baseline, AE-YOLOv13-S achieved lower training loss values: Box Loss declined by 6.76%, Cls Loss by 6.52%, and DFL Loss by 8.65%. On the validation dataset, the model demonstrated reductions of 6.58%, 16.39%, and 8.33% for these respective metrics. After the overall improvements, AE-YOLOv13-S achieved increases of 1.43, 4.85, and 2.69 percentage points in precision, recall, and mAP@50, respectively, with only a 0.3 G increase in FLOPs. The improved model can classify and detect foreign matter in Pu-erh ripe tea efficiently and accurately, providing not only a new technical pathway for foreign matter detection in tea processing but also a practically meaningful technical solution for intelligent quality control and food safety assurance in the tea processing chain."