基于B-YOLO模型的复杂农业场景中的豆检测
Jianjia Qi1, Chunxiang Liu1, Hongxia Chu1
1College of Mechanical and Electrical Engineering, Heilongjiang Institute of Technology, Harbin, China.
Frontiers in plant science
|December 29, 2025
概括
一种新的豆检测模型, kidneyB-YOLO,在复杂的农业环境中显著提高了封闭豆的准确性. 这种先进的模型增强了强度和通用性,以实现高效的收获.
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:
- 检测多个和封闭的豆在开放式田间板种植带来了重大挑战.
- 现有的模型经常与农业环境的复杂性作斗争,影响收获效率.
研究的目的:
- 开发一个先进的目标检测模型,B-YOLO,用于在具有挑战性的农业场景中准确识别豆.
- 为了提高豆检测用于自动收获的稳定性和泛化能力.
主要方法:
- 提出了B-YOLO模型,集成动态卷积,可变形注意力变压器 (DAT),改进了自适应空间特征融合 (ASFF) 和焦点器-SIoU损失函数.
- 开发了一个开放的天花板环境豆数据集用于培训和验证.
- 使用动态卷积来调整自适应参数,使用DAT来集中注意力对关键图像区域.
主要成果:
- B-YOLO模型实现了85.90%的mAP,超过了原来的YOLOv8n,特别是在检测封闭的豆方面.
- 该模型的计算复杂度为12.4 GFLOPs,模型大小为12 MB,速度为32.5 FPS.
- 在豆检测的准确性和稳定性方面取得了显著的改进.
结论:
- B-YOLO模型在复杂的农业环境中为豆检测提供了更高的准确性和稳定性.
- 该模型显示出强大的概括能力,使其适合在露天杆下进行自动化豆收获.
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