中图分类号：TP391.41 文献标志码：A DOI: 10.16157/j.issn.0258-7998.256995 中文引用格式： 把奉昕，燕振刚，陈蕾. SEC-YOLO:一种高效的中草药复杂背景识别模型[J]. 电子技术应用，2026，52(6)：97-105. 英文引用格式： Ba Fengxin，Yan Zhengang，Chen Lei. SEC-YOLO: an efficient complex background recognition model for Chinese herbal medicine[J]. Application of Electronic Technique，2026，52(6)：97-105.
SEC-YOLO： an efficient complex background recognition model for Chinese herbal medicine
Ba Fengxin，Yan Zhengang，Chen Lei
College of Information Science and Technology， Gansu Agricultural University
Abstract： To address the challenges of complex backgrounds, occlusion, and illumination variations in Chinese herbal medicine image recognition, this paper constructs a large-scale dataset containing over 20 000 images of 76 herbal categories and applies various data augmentation techniques to enhance model generalization. Based on YOLO11n, an improved model named SEC-YOLO is proposed, which incorporates the C3k2_Star module, ECA_SR attention mechanism, and C2PSA_CGLU module to improve recognition performance under complex conditions. Furthermore, the feature fusion structure and detection head are optimized to enhance the detection of small and overlapping herbs. The improved model achieves a mean average precision (mAP) of 98.0%, with a weight size of 3.7 MB, 4.7 GFLOPs, and 1.80M parameters. Compared with YOLO11n, the weight size is reduced by 30.1%, FLOPs by 28.7%, and parameters by 31.0%. Both detection accuracy and speed meet the requirements of real-time detection. Experimental results demonstrate that SEC-YOLO achieves high accuracy and real-time recognition while maintaining lightweight characteristics, providing strong support for the automation of Chinese herbal medicine identification.
Key words : Chinese herbal medicine recognition in complex backgrounds；YOLO11；StarNet；channel spatial attention module C2PSA_CGLU；ECA_SR attention mechanism
引言
中草药是中国传统中医的特有药物，在人类的健康中占据着重要的位置，同时在国际上也有着极其强大的威信力[1]。在传统的中药识别过程中，主要依靠人力以及自身经验对中草药加以识别，此种识别方式效率低下并且容易因为人为原因出现误检等情况。尤其在野外采集或复杂背景（如多类药材堆叠、遮挡、光照变化、相似环境等）下，识别难度进一步加大，严重制约了数字中药资源管理及其产业化发展。而人工智能、图像处理和模式识别领域的进步可以拓展和改进中医药技术的实践[2]。近年来，随着深度学习在目标检测领域的迅猛发展，YOLO（You Only Look Once）系列算法因其端到端、一阶段、检测速度快等优势，广泛应用于农业、交通、医疗等图像识别场景。其中，YOLO11作为该系列的最新演进版本，在保持模型轻量化的同时，引入了改进的C3k2模块、ECA_SR注意力机制以及解耦检测头等结构，进一步提升了模型的识别精度与泛化能力。
