现代信息科技2026，Vol.10Issue(8)：83-89,7.DOI:10.19850/j.cnki.2096-4706.2026.08.015
基于改进YOLOv8的轻量化棉种外观品质识别方法
A Lightweight Method for Cotton Seed Appearance Quality Recognition Based on Improved YOLOv8n
摘要
Abstract
In the field of cotton seed appearance quality inspection,the widely adopted detection models are difficult to deploy on resource-constrained edge devices due to their large parameter count and high computational costs.To address this,this paper proposes a lightweight solution based on an improved YOLOv8n.By introducing the EMA Attention Mechanism into the backbone network,the model's ability to capture quality-related visual features is significantly enhanced.The Bottleneck layer in the C2f module is replaced with the lightweight StarBlock module,reducing the model's parameter count and improving computational efficiency.A Bidirectional Feature Pyramid Network(BiFPN)architecture is applied in the neck region,achieving more efficient and adaptive multi-scale feature fusion.Experimental results show that the improved YOLOv8n achieves an average precision of 92.7%,a 1%increase over the original version.The computational complexity is reduced by 0.9 GFLOPs,and the model size is reduced by 37.2%.Compared to other object detection models,this solution demonstrates superior computational efficiency and real-time performance while maintaining high accuracy.关键词
棉种外观品质检测/YOLOv8/注意力机制/轻量化模型Key words
cotton seed appearance quality inspection/YOLOv8/Attention Mechanism/lightweight model分类
信息技术与安全科学引用本文复制引用
曹翱,宋其江..基于改进YOLOv8的轻量化棉种外观品质识别方法[J].现代信息科技,2026,10(8):83-89,7.基金项目
国家自然科学基金(32202147) （32202147）
