在无人机图像中,基于改进的YOLOv8的轻量级小麦耳数模型
Ruofan Li1, Xiaohua Sun2, Kun Yang1
1College of Information Science and Technology, Hebei Agricultural University, Baoding, China.
Frontiers in plant science
|February 26, 2025
概括
一种新的轻量级模型PSDS-YOLOv8,可以在无人机图像中准确地检测小麦穗,从而改善产量预测. 这种方法提高了检测准确度,同时减少了计算负载,以改善农业管理.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 小麦数是小麦产量的关键指标,对农业管理至关重要.
- 精确的小麦耳量化在无人机 (UAV) 图像中具有挑战性,因为目标密集,小,重叠.
- 现有的方法在错过检测,错误阳性和基于无人机的小麦耳数计数精度降低方面扎.
研究的目的:
- 为无人机图像开发一种轻量级和准确的小麦耳朵检测模型.
- 解决当前模型在检测小,密集和重叠的小麦穗方面的局限性.
- 为了提高小麦计数的效率和性能,用于产量估计.
主要方法:
- 提出了一种基于YOLOv8框架的新型轻量级模型PSDS-YOLOv8.
- 引入了高分辨率的微尺度检测层 (P2),并删除了大尺度层 (P5),以优化小目标检测.
- 集成空间金字塔扩展卷积 (SPD-Conv),动态样本 (DySample) 上采样器和空间上下文感知模块 (SCAM) 以增强特征学习和降低计算复杂性.
主要成果:
- PSDS-YOLOv8模型实现了96.5%的mAP50和55.2%的mAP50:95,比基线YOLOv8的表现分别高出2.8%和4.4%.
- 与基线YOLOv8.8相比,模型参数数量减少了40.6%.
- 在准确性和参数效率方面表现出卓越的性能,与其他最先进的模型相比,如YOLOv5,YOLOv7,YOLOv9,YOLOv10,YOLOv11,更快的RCNN,SSD和RetinaNet.
结论:
- 拟议的PSDS-YOLOv8模型有效地提高了无人机图像中的小麦耳探测精度.
- 该模型显著减少了错过和错误检测,同时最大限度地减少了计算资源的消耗.
- 这项研究为智能小麦计数提供了强大的技术解决方案,支持精准农业和产量预测.
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