修剪后重新参数化:基于无人机遥感目标检测的轻量级算法
Yang Yang1, Pinde Song1, Yongchao Wang2
1School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究引入了一种改进的轻量级物体检测算法,用于无人机 (UAV),提高速度和准确性. 精细的模型显著减少了远程传感应用中的错过和重复检测.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 轻物体检测对于无人机在遥感方面至关重要.
- 现有的算法往往以速度换取检测准确度,这对于无人机应用来说是不够的.
- 有需要的算法,既提供高推断速度和精确的检测准确性.
研究的目的:
- 开发一种精细的轻量级物体检测算法,用于无人机遥感.
- 提高现有轻量级模型的推断速度和检测精度.
- 在精度方面解决当前算法的局限性.
主要方法:
- 使用了MobileNetV3以高效通道注意力 (ECA) 模块作为骨干.
- 集成的焦点和高效交叉路口在欧盟 (FocalEIoU) 损失改善回归和减少虚假负数.
- 应用卷积内核修剪,以减少模型参数和FLOP.
- 采用了"插即用"结构重新参数化的微调方法.
主要成果:
- 模型参数和FLOP被减少到1.2M/6.2G (VisDrone) 和1.5M/6.5G (DIOR) 的后修剪.
- 在Jetson AGX Xavier上实现了49 FPS (VisDrone) 和44 FPS (DIOR) 的推断速度.
- 微调将mAP@0.5提高了0.4% (VisDrone) 和mAP@0.5:0.95提高了0.5% (DIOR).
- 在参数,FLOP和mAP指标方面表现优于主流轻量级算法,并显著减少了错过和重复检测.
结论:
- 拟议的精细轻量级物体检测算法为无人机遥感提供了速度和准确性的卓越平衡.
- ECA,FocalEIoU,修剪和重新参数化的组合有效地提高了检测性能.
- 该算法通过在现实世界验证测试中显著减少检测错误来证明其实际实用性.
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