一种基于深度学习的Auricularia auricula外观质量分类方法
Yang Li1,2, Jiajun Hu3, Haiyun Wu1,2
1College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China.
Scientific reports
|July 5, 2024
概括
一个改进的基于快速区域的卷积神经网络 (Faster RCNN) 提高了 Auricularia auricula 的质量分类. 这种方法提高了检测准确度和实时性能,大大提高了不同质量级别的分类.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 农业科学 农业科学
背景情况:
- 准确的外观质量分类Auricularia auricula (木耳) 对于行业的进步至关重要.
- 现有的方法可能缺乏实时工业应用所需的精度和速度.
研究的目的:
- 开发一种智能外观质量分类方法,用于Auricularia auricula.
- 通过使用改进的Faster RCNN框架,提高质量分类的准确性和实时性能.
主要方法:
- 开发了一个改进的基于Faster区域的卷积神经网络 (Faster RCNN) 框架.
- 建立了一个多级特征融合检测模型,以整合浅层和深层特征.
- 该模型通过将其性能与原来的Faster RCNN进行比较来评估.
主要成果:
- 改进的Faster RCNN在平均平均精度 (mAP) 中实现了2.13%的增加.
- 对于第二级 (近5%) 和第三级 (1%) 的 Auricularia auricula.观察到平均精度 (AP) 的显著改善.
- 处理速度从每秒6.81增加到每秒13.5 (FPS).
结论:
- 多级特征融合方法有效地提高了 Auricularia auricula 的检测准确度和速度.
- 改进的Faster RCNN为行业中的智能实时质量分类提供了可行的解决方案.
- 进一步分析探讨了环境复杂性和图像分辨率对检测结果的影响.
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