改进了YOLOv5网络,用于在车载摄像头捕捉场景中实时检测对象
Zuyue Ren1, Hong Zhang1, Zan Li1
1School of Information Engineering, Minzu University of China, Beijing 100080, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究增强了YOLOv5用于驾驶物体检测,改善了道路裂和交通标志识别. 修改后的网络可以更准确地检测道路裂和小型交通标志.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 自主驾驶系统 自主驾驶系统
背景情况:
- 在驾驶过程中对象检测至关重要,但受到环境变化和运动模糊的挑战.
- 传统的方法难以平衡实时性能和高精度.
- 显著的尺度变化和运动模糊冲击检测精度.
研究的目的:
- 提高在驾驶场景中的道路裂和交通标志物体检测的准确性和稳定性.
- 解决传统方法在实现实时检测和高精度方面的局限性.
- 为特定的驾驶相关物体检测任务提供改进的YOLOv5网络.
主要方法:
- 开发了一个改进的YOLOv5网络,将GS-FPN结构与CBAM和Bi-FPN结合起来,用于道路裂检测.
- 引入了一种轻量级的卷积模块 (GSConv),以最大限度地减少信息丢失并增强功能地图.
- 在交通标志检测中采用了四个尺度的特征检测结构,以改善小目标识别.
主要成果:
- 经过修改的YOLOv5网络显示,道路裂检测的平均平均精度 (mAP) 提高了3%.
- 与基线YOLOv5s模型相比,小型交通标志的识别精度提高了12.2%.
- 使用数据增强技术来提高检测网络的整体稳定性.
结论:
- 拟议的YOLOv5增强功能有效地改善了在复杂的驾驶环境中对道路裂和交通标志的物体检测.
- GS-FPN 结构和轻量级卷积有助于更好的特征表示和减少信息丢失.
- 该研究强调了定制深度学习模型在提高道路安全和自动驾驶能力方面的潜力.
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