一种轻量级的多级视觉检测方法用于复杂的交通场景
Xuanyi Zhao1, Xiaohan Dou1, Jihong Zheng2
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究为复杂的交通场景引入了强大的视觉检测框架, 增强了被雾和低光损坏的图像. 该系统提高了车辆和行人检测的准确性,为智能交通系统提供了实用解决方案.
科学领域:
- 计算机视觉
- 人工智能
- 智能交通系统
背景情况:
- 在交通场景中的图像退化 (雾,低照度,遮蔽) 阻碍了对象检测性能.
- 现有的系统在不利的条件下难以稳定地识别车辆和行人.
研究的目的:
- 为复杂的交通环境开发强大的视觉检测框架.
- 为了提高车辆和行人对象检测的准确性,尽管图像退化.
主要方法:
- 使用ConvIR,CIDNet和全新的水平/垂直强度色彩空间策略进行多级图像增强.
- 一种轻量级检测架构,Mamba驱动轻量级检测网络与RT-DETR解码,包含VSSBlock,XSSBlock和VisionClueMerge模块.
主要成果:
- 在交通监控数据集中,拟议的方法实现了mAP@50-90比YOLOv12s增加1.0个百分点 (0.759至0.769).
- 具有优异的部署适应性和稳定性,参数复杂性和计算开销降低.
结论:
- 该框架为在困难的交通条件下对象检测提供了有效的解决方案.
- 集成先进的图像增强和轻量级检测架构可以提高系统的性能和效率.
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