基于轻量级改进的YOLOv5s_AMM模型的多目标核桃外观质量的快速和准确检测
Zicheng Zhan1, Lixia Li1, Yuhao Lin1
1Laboratory of Physical Properties of Agricultural Materials, College of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.
一个优化的YOLOv5s模型 (YOLOv5s_AMM) 改善了核桃质量检测. 它实现了高精度和速度,同时减少了模型大小,使其适合于坚果加工中的边缘设备.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 坚果质量检测在初级加工中至关重要.
- 目前的方法难以快速,高效和准确地识别小坚果.
- 检测小尺寸坚果的精度往往受到损害.
研究的目的:
- 开发一个优化的YOLOv5s模型,以快速准确地识别好和坏的核桃.
- 为了提高模型在检测多个核桃目标在各种规模的性能.
- 为了减少模型大小和提高检测速度,以实现高效的处理.
主要方法:
- 引入了一个优化的YOLOv5s代 (YOLOv5s_AMM) 与一个轻量级的M3-Net取代C3网络.
- 在各种位置内嵌有注意力机制,以提高模型的性能.
- 集成了一个注意力卷积自适应融合模块 (Acmix) 改进了特征提取.
- 用MetaAconC取代SiLU激活功能,以增强多尺度特征检测.
主要成果:
- YOLOv5s_AMM模型实现了平均检测精度 (mAP) 的80.78%,提高了1.81%.
- 模型大小减少到20.9 MB (22.88%压缩),检测速度达到每秒 40.42 .
- 增强型号在准确性,尺寸和多目标检测速度方面始终超过其前身.
结论:
- YOLOv5s_AMM模型在快速,高效和准确地检测多目标核桃质量方面表现出卓越的性能.
- 优化的网络适用于坚果加工行业的轻量级边缘设备.
- 这项研究为在加工过程中检测多目标良性和坏性核桃提供了宝贵的见解.
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