基于融合变压器YOLO的葡萄疾病实时轻量检测
Yifan Liu1, Qiudong Yu1, Shuze Geng1
1College of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
Frontiers in plant science
|March 11, 2024
概括
准确的葡萄疾病检测对于作物产量至关重要. 融合变压器YOLO (FTR-YOLO) 提供了一个实时,轻量级的解决方案,用于使用RGB图像识别四种常见的葡萄疾病.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 葡萄疾病显著降低了作物产量,并可能导致全面的作物失败.
- 快速而准确的疾病鉴定对于有效的疾病管理和最大限度地提高葡萄产量至关重要.
研究的目的:
- 为四种常见的葡萄疾病开发一个实时和轻量级检测模型.
- 提高葡萄疾病在农业环境中识别的准确性和效率.
主要方法:
- 拟议的融合变压器YOLO (FTR-YOLO) 模型包含一个轻量级的VoVNet骨干与幽灵卷曲和SE块.
- 采用了改进的双流 PAN+FPN 子和实时变压器,用于增强小目标检测.
- 实现了脱头,并改进了任务对齐预测器,以实现平衡的准确性和速度.
主要成果:
- FTR-YOLO实现了平均平均精度 (mAP) 的90.67%.
- 该模型的实时处理速度为每秒44 (FPS).
- FTR-YOLO 保持了 24.5M 的轻量级参数大小.
结论:
- FTR-YOLO提供了一种有效的,实时的,轻量级的解决方案,用于检测葡萄疾病.
- 该模型有助于农民及时准确地识别疾病,有助于作物管理.
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