基于轻量级和改进的YOLOv5_SSE模型,快速准确地检测花生外观质量
Zhixia Liu1, Xilin Zhong1, Chunyu Wang1
1College of Engineering, Shenyang Agricultural University, Shenyang, Liaoning, China.
Frontiers in plant science
|March 7, 2025
概括
这项研究优化了YOLOv5s模型以快速准确地检查花生质量,在显著更小的模型尺寸下实现了高检测准确度. 新的SSE-YOLOv5s模型非常适合嵌入式系统,增强农业质量控制.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 手动检查花生外观质量是低效和主观的.
- 自动化系统对于提高花生行业的效率和准确性至关重要.
研究的目的:
- 优化YOLOv5s模型以快速准确地识别花生质量.
- 开发一种自动化系统,用于检测高质量,机械损坏,菌和发芽的花生.
主要方法:
- 优化了YOLOv5s模型,用ShuffleNetv2取代它的骨干,并整合了注意力机制.
- 用焦点-EIoU损失函数来改进边界框回归.
- 优化的模型,SSE-YOLOv5s,在检测准确性和速度方面进行了评估.
主要成果:
- SSE-YOLOv5s实现了99.3%的平均平均精度 (mAP),模型大小为1.1MB.
- 与原来的YOLOv5s相比,优化模型的FPS率高出115.1%.
- 检测准确度达到98.3%,模型参数和计算最小.
结论:
- SSE-YOLOv5s模型在速度,准确性和效率方面提供了卓越的性能,用于多目标花生外观质量检查.
- 它的小型模型尺寸和高FPS使其适合在嵌入式设备上部署.
- 这项研究为花生行业的自动化质量检查提供了宝贵的参考.
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