含有杂质的蛋黄的在线检测方法基于改进的YOLOv7与DeepSORT相结合
Dongjun Gong1,2, Shida Zhao3,4, Shucai Wang1
1College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.
Foods (Basel, Switzerland)
|August 29, 2024
概括
一种新的YOLOv7-SEY-DeepSORT方法准确地检测生产线上盐的蛋中的蛋杂质. 该系统通过实时识别缺陷来提高食品安全,改进了现有的检测技术.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 盐的蛋黄往往含有由于加工而导致的蛋碎片,造成食品安全风险.
- 现有的检测方法可能缺乏生产线所需的实时准确性.
研究的目的:
- 开发一种准确的实时在线检测方法,用于识别盐的黄中的蛋杂质.
- 提高蛋黄生产的食品安全和加工效率.
主要方法:
- 使用了完善的YOLOv7对象检测模型,与DeepSORT集成用于跟踪.
- 集成的坐标注意 (CA) 模块和焦点-EIoU损失功能,用于改进杂质检测.
- 用MobileOne取代骨干,以提高实时性能和减少模型大小.
主要成果:
- 实现了0.931的平均平均精度 (mAP),比原来的YOLOv7.3提高了0.53%.
- 证明了优越的跟踪性能,MOTA为87.9%和MOTP为73.8%.
- 在综合性能方面表现优于其他主流物体检测方法.
结论:
- YOLOv7-SEY-DeepSORT方法提供了一个强大的解决方案,可以快速准确地检测有缺陷的盐蛋.
- 为蛋产品的自动化和安全加工提供技术基础.
- 平衡高检测准确度与实时处理能力.
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