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基于YOLO的增强框架,用于准确检测和识别带有明显物体的普通小麦杂质

Hossein Bagherpour¹, Negar Fattahi Peyruo²

¹Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran. h.bagherpour@basu.ac.ir.

Scientific reports

|November 18, 2025

概括

这项研究评估了用于实时检测小麦杂质的YOLO模型. YOLOv5n为实时应用提供了最佳的速度精度平衡,而较大的YOLO模型适合实验室分析.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
食品科学食品科学食品科学

背景情况:

准确检测小麦谷物杂质对于储存,磨削和收获至关重要.
实时检测需要与处理速度平衡精度.

研究的目的:

评估YOLOv5n,YOLOv5x,YOLOv8n和YOLOv8x模型用于检测小麦粒杂质的性能.
为实验室和实时应用确定最佳算法和分辨率.

主要方法:

在700个标记图像上训练了四个YOLO模型 (YOLOv5n,YOLOv5x,YOLOv8n,YOLOv8x) 在三个分辨率上.
基于检测准确度 (mAP@50) 和处理速度的评估模型.

主要成果:

较大的YOLO模型 (YOLOv5x,YOLOv8x) 在实验室应用中显示出类似的性能,但速度降低.
YOLOv5n在320x320分辨率下提高了4%的检测速度,同时保持了准确性,非常适合实时使用.
对视觉上类似的杂质 (85-88%) 和其他 (>95%) 实现了高mAP@50 .

结论:

对于非破坏性小麦杂质检测,YOLO模型是有效的.
模型选择取决于应用需求:实时的YOLOv5n,实验室分析的YOLOv5x/YOLOv8x.
该方法可以适应在其他谷物中检测杂质.

关键词:

人工智能的人工智能是人工智能.对象检测检测对象检测对象检测小麦杂质小麦杂质这就是YOLOv5n.

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