SDA-YOLO:一种在复杂的果园环境中对桃子进行物体检测的方法
Xudong Lin1, Dehao Liao1, Zhiguo Du1
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究介绍了SDA-YOLO,这是一个改进的果园桃子检测方法. SDA-YOLO通过整合新的模块来提高复杂环境中的精度,以更好地表示和定位特征,帮助智能收获水果.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 果园中的桃子检测面临着诸如遮蔽,复杂的背景和尺度变化等挑战.
- 现有的方法在这些困难条件下难以准确识别桃子.
研究的目的:
- 开发一种改进的桃子检测方法,SDA-YOLO,基于YOLOv11n,用于复杂的果园环境.
- 为了增强特征表示,本地化准确性和特征融合灵活性,用于桃子检测.
主要方法:
- 在SPPF中集成的LSKA模块用于多尺度特征表示 (SPPF-LSKA).
- 使用的MPDIoU损失,以改善封闭桃子的边界框回归.
- 在检测头 (DMDetect) 中内置DyHead块,以更好地进行特征歧视.
- 引入了自适应式多尺度融合金字塔 (AMFP) 模块,以提高部网络的灵活性.
主要成果:
- SDA-YOLO的精度达到了90.8%,回忆率达到了85.4%,mAP@0.95达到了90%,mAP@0.5:0.95达到了62.7%.
- 与基线YOLOv11n相比,显著改善,分别增加了2.7%,4.8%,2.7%和7.2%.
- 在复杂的果园环境中经过验证的坚固性.
结论:
- 在具有挑战性的果园环境中,SDA-YOLO为桃子检测提供了强大的解决方案.
- 该方法为智能水果收获和产量估计提供了有效的技术支持.
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