基于改进的YOLOv5-AT的基础上,在类似的颜色背景下用一个小数据集检测绿色水果
Xinglan Fu1, Shilin Zhao1, Chenghao Wang1
1College of Engineering and Technology, Southwest University, Chongqing 400716, China.
Foods (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种改进的YOLOv5模型 (YOLOv5-AT),用于在小型数据集中准确检测绿色水果,即使背景颜色相似. 该模型提高了农业应用中的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 准确的绿色果实检测对于果园管理至关重要 (产量估计,资源分配).
- 现有的方法与小数据集,相似的水果/背景颜色和复杂的背景作斗争.
- 对于此类任务,YOLO物体检测模型在速度和多功能性方面具有优势.
研究的目的:
- 为小型数据集开发一个准确和方便的绿色水果检测方法.
- 为了应对绿色水果和背景之间的相似颜色所带来的挑战.
- 通过增强检测能力,提高农业生产的效率.
主要方法:
- 提出了一个改进的YOLOv5模型 (YOLOv5-AT),包含数据增强技术.
- 引入了一个Conv-AT块和SA/CA块,以增强特征表示和信息流.
- 在绿色子,西红和红的数据集上测试了模型.
主要成果:
- YOLOv5-AT模型实现了高平均精度 (mAP):绿色子的平均精度为84.6%,绿色番茄的平均精度为98.0%,绿色红豆的平均精度为85.1%.
- 在小型数据集上表现出有效性,在50%的培训数据中,绿色子实现了82.2%的mAP.
- 在检测类似颜色背景的绿色水果方面表现优于其他YOLO物体检测模型.
结论:
- 与数据增强相结合的YOLOv5-AT模型,在挑战小型数据集时,为绿色水果检测提供了有效的解决方案.
- 该方法显著提高了检测准确性和稳定性,即使数据有限.
- 结果支持通过精确的果实检测提高农业生产的效率.
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