边缘智能轻量级视觉系统,用于在养子养的子中进行视角校正的蛋匹配
Dakang Guo1, Jiatao Wang1, Zeyuan Lin1
1College of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China; Guangdong Engineering Research Center of Agricultural Big Data,Guangzhou 510642, PR China.
Poultry science
|January 23, 2026
概括
这项研究引入了一种移动摄像系统,用于在养中自动计数蛋,从而提高精确的繁殖管理. 该系统准确地检测卵子,并使用先进的AI模型和跟踪算法将它们与子匹配.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 对个体卵产量进行监测对于优化子养的繁殖管理至关重要.
- 现有的多传感器系统是昂贵的,固定摄像头的覆盖范围有限,需要创新的解决方案.
研究的目的:
- 开发一个经济高效的,基于移动摄像头的系统,用于自动化监测产卵的个体卵产量.
- 为了提高蛋检测,子识别和蛋子匹配的准确性和效率.
主要方法:
- 开发了一种轻量级的蛋和QR码检测 (LDEQ-OD) 模型,该模型基于改进的YOLOv11框架,具有双检测头 (DH) 和C3-DDF模块.
- 采用了OC-SORT用于多对象跟踪和布强大的QR码解码 (CRQD) 算法来增强QR码识别.
- 实施了最小尺寸比率偏差 (MARD) 动态匹配策略,以弥补几何扭曲.
主要成果:
- 在边缘设备上,LDEQ-OD模型实现了高精度 (99.6%的精度,99.3%的回忆,95.3%的mAP@0.5:0.95) 与快速推断 (59.2毫秒).
- 与传统解码器相比,CRQD显著提高了代码识别率,从72.7%提高到99.3%.
- 该MARD策略的结果是高卵匹配精度 (ECMA) 为98.3%,平均绝对误差 (MAE) 为0.017个每蛋的低.
结论:
- 拟议的移动摄像头监控系统能够智能地实时跟踪养子中的个体蛋产量.
- 这项技术通过提供准确和高效的数据采集,支持精确的育种管理.
- 该系统在各种条件下表现出强大的性能,包括运动模糊,不均的照明和复杂的几何视角.
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