DGS-Yolov7-Tiny:适用于边缘计算环境的轻量级害虫和疾病目标检测模型
Ping Yu1,2,3, Baoshu Zong4,3, Xiaozhong Geng1,3
1School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, 130012, China.
Scientific reports
|August 14, 2025
概括
一个新的轻量级害虫检测模型,DGS-YOLOv7-Tiny,为智能农业提供实时作物监测. 这种高效的解决方案提高了边缘设备上的虫害识别准确度.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 传统的害虫检测模型是计算密集型的,限制了边缘计算中的实时使用.
- 有效和准确的害虫检测对于现代农业和作物健康管理至关重要.
研究的目的:
- 为边缘计算环境开发一个优化的轻量级害虫检测模型.
- 在智能农业应用中提高害虫检测的精度和效率.
主要方法:
- 建议DGS-YOLOv7-Tiny,这是一个基于YOLOv7-Tiny的轻量级模型.
- 整合了一个全球关注模块,用于增强上下文聚合和小物体检测.
- 引入了DGSConv,一种新的融合卷积,以减少参数,同时保留特征信息.
- 用SiLU取代泄漏的ReLU,用SIOU取代CIOU,以改善梯度流和收速度.
主要成果:
- 在番茄叶虫害数据集上,DGS-YOLOv7-Tiny获得了95.53%的精度,92.88%的召回率和96.42%的mAP@0.5.
- 该模型有443万个参数和10.2 GFLOPs的计算复杂度.
- 实现了168 FPS的高推理速度,证明了适用于边缘计算的适用性.
结论:
- DGS-YOLOv7-Tiny在害虫检测效率和边缘设备计算要求方面提供了显著的改进.
- 该模型为智能农业中的实时病虫检测提供了实用和有效的解决方案.
- 这项研究对于推进农业技术具有相当大的理论和实践价值.
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