改进了基于YOLOv5的GBS-YOLOv5算法,应用于无人机智能交通
Haiying Liu1, Xuehu Duan2, Haitong Lou2
1The School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Shandong, China. haiyingliu2019@qlu.edu.cn.
Scientific reports
|June 13, 2023
概括
我们开发了GBS-YOLOv5,这是一个改进的无人机检测算法,提高了交通管理中的小目标准确性. 与标准YOLOv5.5相比,这种新型号显著提高了检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 交通管理系统 交通管理系统
背景情况:
- 越来越复杂的道路交通需要先进的管理解决方案.
- 无人驾驶飞行器 (UAV) 提供了交通监控的潜力,但在小目标检测准确度方面存在困难.
- 现有的YOLOv5模型在深度特征提取和浅度特征利用中面临信息丢失的挑战.
研究的目的:
- 在交通管理中,提高无人机对小型目标的检测准确度.
- 解决YOLOv5模型在小物体检测和功能利用方面的局限性.
- 为了引入一种新的算法,GBS-YOLOv5,优化用于基于无人机的交通监控.
主要方法:
- 设计了一种高效的时空交互模块,以加深特征提取.
- 集成了一个空间金字塔卷积模块,以增强小目标信息挖掘.
- 提出了一个浅的瓶,具有递归门卷积,以改善特征融合和细节保存.
主要成果:
- 通过GBS-YOLOv5算法实现了35.3%的mAP@0.5和20.0%的mAP@0.5:0.95.
- 在mAP@0.5和mAP@0.5:0.95分别表现出4.0%和3.5%的性能改善,相比于标准YOLOv5.5.
- 验证了拟议模块在改善无人机小型目标检测方面的有效性.
结论:
- 在复杂的交通场景中,GBS-YOLOv5显著提高了无人机在小型目标检测准确度.
- 新的架构修改有效地解决了信息丢失问题,并提高了深度学习模型中的功能利用率.
- 这种算法为利用空中监视的智能交通管理系统提供了有前途的进步.
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