一种基于改进的YOLOv5s的生长期轻量级识别方法
Kaixuan Liu1, Jie Wang2, Kai Zhang1
1College of Engineering, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
一个新的轻量级AI模型,Small-YOLOv5,准确地识别了大米生长阶段. 与手工观察相比,这种方法提高了效率,减少了主观性,有助于高产大米种植.
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
- 用于作物监测的深度学习
背景情况:
- 准确识别米生长阶段对于优化产量和质量至关重要.
- 目前的手动观察方法是低效和主观的.
- 需要自动化方法来克服手工米生长阶段识别的局限性.
研究的目的:
- 开发一种轻量级和高效的深度学习模型,用于自动识别生长期.
- 提高大米生长阶段识别的速度和准确性.
- 为了减少实际部署的计算成本和模型大小.
主要方法:
- 提出了小YOLOv5,这是一个改进的YOLOv5s架构.
- 集成的MobileNetV3作为减少模型大小和更快检测的骨干.
- 在功能融合阶段实现了轻量级卷积GsConv,以减少计算复杂性,同时保持学习能力.
主要成果:
- 与YOLOv5s相比,小YOLOv5实现了模型参数减少82.4%,GFLOPS减少85.9%,尺寸减少86.0%.
- 该模型显示平均平均精度 (mAP) 为98.7% (在0.5 IoU),仅比原始YOLOv5s.s.低0.8%.
- 与YOLOV5s-MobileNetV3-Small相比,小YOLOv5显示了10.0%的参数减少,9.6%的体积减少,5.0%的mAP改善 (至94.7%) 和1.5%的回忆增强 (至98.9%).
结论:
- 小YOLOv5提供了一个高效和优越的轻量级解决方案,用于自动识别米生长期.
- 该模型的效率和准确性使其适用于精密农业的实际应用.
- 开发的方法大大克服了在水种植管理中手动观察的局限性.
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