基于边缘计算的农业自动化实时对象检测:比较YOLOX和YOLOv12架构及其在土豆收获系统中的性能的案例研究
Joonam Kim1, Giryeon Kim1, Rena Yoshitoshi1
1Research Center for Agricultural Robotics, National Agricultural and Food Research Organization, Tsukuba 3050856, Japan.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
YOLOX为农业自动化提供了卓越的速度和能源效率,而YOLOv12在检测不平衡数据集中具有挑战性的对象方面表现出色. 这两种模型都实现了高精度,但对于边缘计算来说,实现是很重要的.
科学领域:
- 计算机视觉 计算机视觉
- 农业自动化 农业自动化
- 边缘计算 边缘计算
背景情况:
- 像YOLOX和YOLOv12这样的物体检测模型对于农业自动化至关重要.
- 边缘计算平台,如Jetson AGX Orin,可以在农业环境中实现实时处理.
- 农业数据集中的数据不平衡给模型性能带来了挑战.
研究的目的:
- 在Jetson AGX Orin平台上对农业自动化物体检测模型YOLOX和YOLOv12进行比较分析.
- 评估架构差异对数据不平衡环境中的检测能力的影响.
- 为农业自动化系统中模型选择提供指导方针.
主要方法:
- 对象检测模型YOLOX和YOLOv12的比较分析.
- 在相同的土豆,土壤块和石头数据集上进行培训和评估.
- 在受控条件下使用30项独立试验进行性能评估.
- 统计分析和NVIDIA Nsight对性能和实施评估进行分析.
主要成果:
- 与YOLOv12 (45 FPS,0.75 J/frame) 相比,YOLOX显示了显著更高的吞吐量 (107 FPS) 和能源效率 (0.58 J/frame).
- 两种模型都实现了相当的总体检测准确度 (F1得分,0.97).
- YOLOv12对土壤块 (0.725比0.512) 的回忆力更强,对小物体的精度更高.
- 在YOLOv12的多进程架构中实现的低效率限制了它的边缘性能.
结论:
- YOLOX更适合用于需要高速和能源效率的实时农业自动化.
- YOLOv12的架构为处理数据不平衡和检测具有挑战性的对象提供了优势,但需要优化边缘部署.
- 模型选择必须考虑架构能力,实施效率和农业自动化中的特定应用要求之间的权衡.
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