基于改进的YOLOv5-seg模型的苦瓜成熟度的细粒度识别
Sheng Jiang1, Jiangbo Ao1, Hualin Yang2
1College of Electronic Engineering, South China Agricultural University, Guangzhou, 510642, China.
Scientific reports
|May 13, 2024
概括
准确的苦瓜收获对于质量和产量至关重要. 一个改进的YOLOv5-seg模型具有动态蛇形卷积和Focal-EIOU损失,可以准确地实时分割苦的生长阶段.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 精准农业 精准农业 精准农业
背景情况:
- 苦的易腐坏性需要精确的收获,以获得最佳的质量和产量.
- 目前的收获依赖于主观经验,导致效率低下和潜在的损失.
- 准确评估苦瓜生长阶段对于及时干预和产量最大化至关重要.
研究的目的:
- 开发一个改进的实时实例细分模型,用于苦瓜生长阶段识别.
- 为了提高苦收获决策的准确性和效率.
- 解决主观经验在确定苦成熟度方面的局限性.
主要方法:
- 基于YOLOv5-seg. 的改进实时实例细分模型的实施.
- 利用动态蛇卷积来提取苦瓜的形态特征.
- 纳入多种分支区块以增强功能多样性而不会增加模型大小.
- 应用焦点-EIOU损失来准确的界限框和面具定位,解决样本不平衡.
主要成果:
- 使用mAP@0.5.5.3实现了99.3% (L1),93.8% (L2) 和98.3% (L3) 的显著准确率.
- 与其他实例细分模型相比,在检测准确性和推断速度方面表现出卓越的性能.
- 在不同的照明和遮蔽条件下,成功地实时对苦瓜进行了细分.
结论:
- 改进的YOLOv5-seg模型在扩张阶段提供了精确高效的细粒度识别.
- 该模型提供可靠的实时到期信息,为农业工人提供精确的收获决策.
- 这项技术提高了农业效率,并减少了苦瓜生产的收获后损失.
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