改进玉米作物行导航线识别方法YOLOX的玉米作物行导航线识别方法
Hailiang Gong1, Weidong Zhuang1, Xi Wang1
1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
Frontiers in plant science
|April 12, 2024
概括
一个改进的YOLOX-Tiny模型准确地识别了玉米作物排列,用于智能除草机械,在具有挑战性的条件下增强了导航. 这提高了农业生产率,减少了除草剂的使用.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的玉米作物排列识别对于自主农机导航至关重要.
- 照明变化和复杂的现场条件对现有的检测方法构成重大挑战.
研究的目的:
- 开发一个优化的YOLOX-Tiny模型,用于强大的玉米作物行导航线检测.
- 提高智能除草机械引导系统的准确性和效率.
主要方法:
- 一个优化的YOLOX-Tiny模型,包含自适应照明调整和多尺度预测.
- 视觉注意力机制 (高效通道注意力,合作注意力) 和快速空间金字塔聚合的整合.
- 使用了对欧盟损失函数的坐标交叉点和作物行配合的最小方程方法.
主要成果:
- 实现了92.2%的平均精度,检测时间为15.6毫秒,比原始模型提高了16.4%.
- 模型尺寸缩小了7.1%至18.6 MB.
- 证明了高效的作物排列配合,处理时间为42ms,平均角度误差为0.59°.
结论:
- 增强的YOLOX-Tiny模型为玉米作物排行导航提供了高度准确和高效的解决方案.
- 这项技术显著支持智能除草机械,促进可持续农业和提高作物产量.
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