Objective To improve the YOLOv8 model and provide a high-accuracy and high-efficiency automated detection solution for Chinese herbal slices in practical scenarios such as production, dispensing, and teaching. Methods Taking YOLOv8 as the baseline model, a lightweight residual depthwise-attention Bottleneck (RDA-Bottleneck) is proposed to replace the Bottleneck blocks in C2f, reduce redundant computations and enhance the expression of channel features. To strengthen the discriminative capability of the detector during multi-scale feature learning, a frequency-aware spatial attention (FASA) module is introduced to replace the Conv blocks in the Backbone and Neck of YOLOv8. To evaluate the performance of the improved model, a dedicated dataset containing 10 categories of Chinese herbal slices [e.g., Banlangen (Isatidis Radix) and Gancao (Glycyrrhizae Radix et Rhizoma)] is constructed, comprising 8 281 images. Results Compared with YOLOv8, the proposed model reduces parameters by 39.5% and floating point operations (FLOPs) by 34.1%, while improving single-threshold average accuracy mAP50 by 0.2% and multi-threshold average accuracy mAP50-95 by 0.2%. Conclusion The improved model achieves higher detection accuracy and inference efficiency for Chinese herbal slice detection under complex backgrounds and diverse appearances, providing an effective method for automatic detection of Chinese herbal slices.

Chinese herbal slices detection  /  YOLOv8  /  lightweight object detection  /  attention mechanism  /  residual depthwise-attention Bottleneck  /  frequency-aware spatial attention