Authors

Abstract

To achieve automated intelligent sorting of tobacco-coated seeds, this study proposes a lightweight classification model, EMC-YOLO, based on an improved YOLOv10n. This model optimizes YOLOv10n in three aspects: first, replacing the original backbone network with EfficientViT, balancing low computational requirements and global feature extraction capabilities; second, introducing an improved multi-scale spatial pyramid attention (MSPA) into the neck network, utilizing parallel dilated convolution to optimize feature fusion; third, employing the CARAFE operator for upsampling to enhance the edge restoration effect of small targets. Experiments show that the precision, recall, and mAP@0.5 of EMC-YOLO reach 95.12%, 91.20%, and 93.01%, respectively, representing improvements of 2.9, 2.2, and 2.07 percentage points over the original model. Simultaneously, the model reduces the number of parameters by 71%, computational load by 75%, and weight volume by 68.6%. This model achieves high-performance detection with low resource consumption, providing a technical reference for automated seed sorting.

Abstract in Chinese

为实现烟草包衣种子的自动化智能分选，本研究提出一种基于改进YOLOv10n的轻量级分类模型EMC-YOLO。该模型通过三方面优化YOLOv10n：第一，使用EfficientViT替换原主干网络，兼顾低计算量与全局特征提取能力 ；第二，在颈部网络引入改进的多尺度空间金字塔注意力（MSPA），利用并行空洞卷积优化特征融合 ；第三，采用CARAFE算子进行上采样，提升小目标的边缘恢复效果 。实验显示，EMC-YOLO的精确率、召回率及mAP@0.5分别达到95.12%、91.20%和93.01%，较原模型提升了2.9、2.2和2.07个百分点 。同时，模型参数量减少71%，计算量降低75%，权重体积缩小68.6% 。该模型能在低资源消耗下实现高性能检测，为种子自动化分选提供了技术参考。