实时检测果园中的类品种,基于复杂的情景,改进了YOLOv7的实时检测
Fuqin Deng1, Jianle Chen1, Lanhui Fu1
1School of Electronic and Information Engineering, The Wuyi University, Jiangmen, China.
Frontiers in plant science
|July 16, 2024
概括
一个新的YOLOv7-BiGS模型增强了类品种检测,在复杂的果园条件下提高了5%以上的准确性. 这种方法支持干燥皮生产的高效选择.
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:
- 果品种检测对于生产高品质的新干皮至关重要.
- 果园中的物体检测面临着挑战,因为叶子和果实之间的相互遮.
研究的目的:
- 引入一种有效的果品种检测方法,YOLOv7-BiGS,以提高选效率.
- 增强特征提取和简化模型复杂性,用于类植物识别.
主要方法:
- 开发了一个包含BiFormer注意力机制和GSConv卷积的YOLOv7-BiGS模型.
- 使用精度,mAP和回忆指标,将YOLOv7-BiGS与YOLOv5,YOLOv7和YOLOv8进行了比较.
- 在花园环境中对封闭的新类果图像进行了模型的性能评估.
主要成果:
- YOLOv7-BiGS实现了91%的精度,93.7%的mAP和87.3%的回忆.
- 与基线方法相比显著改善,精度,mAP和召回分别增加了5.8%,4.8%和5.2%.
- 在复杂的环境条件下验证的有效性与封闭的水果图像.
结论:
- YOLOv7-BiGS方法为有效的类品种识别提供了强大的解决方案.
- 这项研究为农业应用中品种检测提供了至关重要的技术支持.
- 该模型的性能突出显示了其在果生产中实际使用的潜力.
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