一个新的轻量级深度学习模型用于微观图像中的猪精头检测:YOLO11_SRP
Mingchao Pan1, Lin Gao1, Zhendong Zhu2
1College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
Animals : an open access journal from MDPI
|January 28, 2026
概括
一个新的轻量级模型,YOLO11_SRP,在显微镜图像中准确检测野猪精子头. 这种自动化方法改进了现有技术,为生殖管理提供了效率.
科学领域:
- 兽医科学 兽医科学 兽医科学
- 生物技术是生物技术.
- 计算机视觉 计算机视觉
背景情况:
- 准确的猪精头检测对于动物繁殖和生殖管理至关重要.
- 手动计数是主观和低效的;现有的自动化方法在高放大图像中与重叠或快速移动的精子作斗争.
研究的目的:
- 为复杂的显微场景开发一种轻量级和精确的猪精子检测模型 (YOLO11_SRP).
- 提高小物体识别能力,以改善自动化精子分析.
主要方法:
- 提出了一种新的轻量级野猪精子检测模型,YOLO11_SRP.
- 整合了StarNet的骨干,一个矩形的自我校准模块,以及针对微小目标的低级检测层.
- 在野猪精子显微镜图像数据集上评估模型,并将其与标准YOLO11s框架进行比较.
主要成果:
- YOLO11_SRP实现了91.9%的平均平均精度 (mAP@0.5),比YOLO11s有13.9%的改进.
- 与YOLO11s.相比,该模型将参数降低了39%,计算成本降低了14.1%.
- 在检测小型和重叠的野猪精子头方面表现出卓越的性能.
结论:
- YOLO11_SRP为野猪精子头检测提供了一种高效准确的解决方案.
- 该模型支持开发可靠的自动化精子分析管道.
- 这项技术可以在繁殖选择和生殖管理方面发挥重要作用.
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