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基于深度学习算法对肉部分的自动分类和检测的研究.
Yan Chen1, Xianhui Peng1, Lu Cai1
1School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China.
Journal of food science
|September 1, 2023
概括
这项研究引入了使用YOLOV4深度学习的实时部分分类和检测方法,显著提高了家禽加工效率. YOLOV4-CSPDarknet53模型实现了98.86%的mAP,优于其他模型并缩短了处理时间.
科学领域:
- 计算机视觉和机器学习
- 农业技术 农业技术
- 食品加工自动化 食品加工自动化
背景情况:
- 准确的部分识别对于家禽加工效率至关重要.
- 重叠的肉部分对当前的识别系统构成了重大挑战.
- 现有的方法缺乏高速装配线所需的实时精度.
研究的目的:
- 开发一种实时分类和检测肉部位的方法.
- 为了应对在自动化加工中重叠的肉部分的挑战.
- 为了提高生产率和减少家禽加工厂的浪费.
主要方法:
- 创建并增强了600个部位图像的数据集.
- 使用YOLOV4深度学习模型 (YOLOV4-CSPDarknet53) 来进行对象检测.
- 使用平均平均精度 (mAP) 和推断速度评估性能,与YOLOV3和SSD模型进行比较.
主要成果:
- YOLOV4-CSPDarknet53模型实现了98.86%的mAP,推断速度为22.2 ms.
- 这种性能在准确性和速度上都超过了比较型号 (YOLOV3,SSD).
- 该方法成功地实时识别了细分的肉部分,即使有遮.
结论:
- 拟议的基于YOLOV4的方法为实时肉部分分类和检测提供了高度准确和高效的解决方案.
- 这项技术可以显著减少浪费,提高家禽加工的资源利用率.
- 该系统提供视觉技术援助,以优化对消费者偏好的分类,切割和餐饮.
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