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基于计算机视觉的深度学习建模用于鱼部分细分和缺陷识别.

Chunxu Zhang1, Yuanshan Zhao2, Wude Yang2

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310027, China.

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概括
此摘要是机器生成的。

一个新的机器视觉系统准确地切割鱼部分,并使用双阶段融合网络检测缺陷. 这种自动化方法显著提高了鱼加工的准确性和效率,推进了工业自动化.

关键词:
检测缺陷检测检测缺陷检测的方法机器视觉 机器视觉 机器视觉部分细分部分细分部分.这就是鱼,鱼,鱼.两个阶段的模型模型.

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科学领域:

  • 水产养殖技术 水产养殖技术
  • 计算机视觉 计算机视觉
  • 食品加工自动化 食品加工自动化

背景情况:

  • 手动检查鱼部分和检测缺陷限制了工业加工效率和准确性.
  • 目前的方法很难满足大规模的,自动化的鱼加工需求.

研究的目的:

  • 开发一种自动化机器视觉检查方法,用于精确切割鱼部分和检测缺陷.
  • 通过提高准确性和效率,提高鱼加工的附加值.

主要方法:

  • 建立了一个视觉检查系统来收集鱼体图像,然后进行数据集预处理和增强.
  • 改进的U-Net模型与CBAM注意力机制被用于鱼部分细分.
  • 实现了将YOLOv5与改进的U-Net相结合的两级融合架构,用于缺陷检测和本地化.

主要成果:

  • 改进的U-Net实现了96.87%mAP和94.33%mIoU的零件切割,超过了基础模型.
  • 用于缺陷检测的融合模型达到94.28%的mAP,处理速度为7.30fps.
  • 与单个U-Net模型相比,拟议的方法在准确度 (28.02%) 和效率 (236.4%) 方面都取得了显著的改进.

结论:

  • 开发的机器视觉方法为智能鱼加工提供了高精度,高效的解决方案.
  • 这种方法大大提高了水产品加工行业的自动化.
  • 两个阶段的融合网络有效地解决了鱼部分切割和缺陷识别方面的挑战.