提高基于视觉的交通事故检测性能在日夜场景中的一致性:深度感知和域适应性网络
Yang Yang1, Xiantian Chen1, Jianyu Wang2
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Accident; analysis and prevention
|January 16, 2026
概括
这项研究引入了一种新的深度感知网络,用于在各种照明条件下强大的交通视频撞车检测. 视觉状态空间模型 (VSSM) 提高了全天交通安全监控的准确性和速度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 交通安全系统 交通安全系统
背景情况:
- 目前基于闭路电视 (CCTV) 的交通视频撞车检测系统在不同的照明条件下 (白天与夜晚) 显示性能下降.
- 这种性能差距阻碍了可靠的全天事故响应和救援效率.
研究的目的:
- 开发一个强大且适应领域的网络,用于在异质照明环境中准确检测交通事故.
- 提高交通视频分析的稳定性和一致性,以提高安全性.
主要方法:
- 提出了一个基于视觉状态空间模型 (VSSM) 的深度感知和域适应网络.
- 采用双流架构,集成外观,运动和3D深度信息.
- 整合了空间几何学的深度增强模块和域适应约束,以减轻域转移.
主要成果:
- 实现了高性能,96.043%的回忆率,2.507%的错误率和97.003%的F1分数.
- 与基线模型相比,表现出明显的超越性.
- 实现了118 FPS的实时推断速度,计算成本低 (0.623 GFLOPs).
结论:
- 拟议的框架有效地减轻了白天和夜间撞车检测之间的性能差异.
- 高计算效率和精度有助于更快的应急响应和减少伤亡风险.
- 为稳定和可转移的智能交通安全监控系统提供实用基础.
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