目的 解決中藥飲片種類繁多、形態(tài)相似，人工識(shí)別耗時(shí)費(fèi)力且易出錯(cuò)的問(wèn)題。方法 構(gòu)建了包含 201 類中藥飲片的數(shù)據(jù)集，并提出了一種輕量化改進(jìn)的 YOLOv8 算法，具體改進(jìn)包括在 YOLOv8n 網(wǎng)絡(luò)中引入 GhostC2f 模塊以降低模型參數(shù)量， 采用 DySnakeC2f 模塊以增強(qiáng)對(duì)纖細(xì)結(jié)構(gòu)的靈敏度，替換主干網(wǎng)絡(luò)的池化層為 SimSPPF 模塊以加快推理速度，并加入坐標(biāo)注意力（ coordinate attention， CA）機(jī)制以增強(qiáng)對(duì)小尺寸目標(biāo)的特征提取。結(jié)果 改進(jìn)后的算法跨閾值平均精度（ 50%～95%）達(dá)到 84.16%，較之前提高了 4.39%，同時(shí)模型參數(shù)量減少了約 15%。改進(jìn)的模型成功部署在電腦客戶端和手機(jī) APP中，構(gòu)建了中藥飲片自動(dòng)化識(shí)別標(biāo)注系統(tǒng)。結(jié)論 改進(jìn)后的模型能夠有效識(shí)別中藥飲片，同時(shí)系統(tǒng)支持自動(dòng)數(shù)據(jù)擴(kuò)充和升級(jí)，從而為中藥飲片的快速、準(zhǔn)確識(shí)別提供了一種新方法。

Objective Addressing the issues of the diverse types and similar shapes of traditional Chinese medicine decoction pieces (TCMDPs), which could make manual identification time-consuming, labor-intensive, and prone to errors. Methods A dataset containing 201 classes of TCMDPs was constructed, and a lightweight improved YOLOv8 algorithm was proposed. The specific improvements include introducing the GhostC2f module in the YOLOv8n network to reduce model parameters, adopting the DySnakeC2f module to enhance sensitivity to fine structures, replacing the pooling layers of the backbone network with SimSPPF to accelerate inference speed, and incorporating the coordinate attention (CA) mechanism to improve feature extraction for small-sized targets. Results The improved algorithm achieved a cross-threshold mean average precision (50%—95%) of 84.16%, representing an increase of 4.39% compared to the previous version, while reducing the model’s parameter count by approximately 15%. The enhanced model was successfully deployed on both computer clients and mobile apps, creating an automated recognition and annotation system for TCMDPs. Conclusion The improved model effectively identifies TCMDPs, while the system supports automatic data expansion and upgrades, providing a novel approach for rapid and accurate identification of TCMDPs.

TP18;R282.710.3

中國(guó)中醫(yī)科學(xué)院科技創(chuàng)新工程項(xiàng)目(CI2023E002);國(guó)家自然科學(xué)基金青年科學(xué)基金項(xiàng)目(32202415);山東省研究生優(yōu)質(zhì)教育教學(xué)資源項(xiàng)目(SDYAL2022041)