蛋管理的智能系统:整合了一种新的轻量级YOLO模型和多融合,用于稳健的检测和定位
Yufan Cheng1, Yao Liu1,2, Qianhui Li1
1College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing 211800, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了一种用于自动蛋识别和定位的智能系统,利用改进的YOLOv12n算法和OpenCV. 该系统显著提高了子蛋养殖管理的效率和准确性.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 蛋养殖面临着高破损率和劳动力成本的挑战.
- 目前的手动管理方法效率低下,容易出现错误.
研究的目的:
- 开发一种智能系统,用于自动识别和定位蛋.
- 为了降低蛋养殖中的破损率和劳动力成本.
- 提高子蛋管理的效率和准确性.
主要方法:
- 使用了改进的YOLOv12n对象检测算法来识别子蛋.
- 采用OpenCV条形码识别技术来提取位置信息.
- 收集实时视频数据,注释图像,并训练一个自定义数据集 (1500培训,215验证,215测试图像).
- 实施了数据增强和多数投票方法,以提高检测准确度.
主要成果:
- YOLOv12n-pg模型实现了低计算负载 (4.9 GFLOPS),1.56 M参数,以及一个小模型大小 (3.5 MB).
- 推断测试产生了高性能指标:99.4%mAP50和83.6%mAP50-95.5.
- 该系统成功记录了"子位置-卵数"数据,证明了错过检测率的降低.
结论:
- 开发的系统为自动化蛋管理提供了有效的解决方案.
- 它显著提高了识别性能,效率和准确性,而不是手动方法.
- 为未来智能家禽养殖系统的进步提供了基础.
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