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评估行人交叉路口的安全性:在配对的空中和主观视角图像上训练的卷积神经网络模型的实施和评估
Dylan Russon1, Antoine Guennec1, Juan Naredo-Turrado1
1University of Bordeaux, INSERM BPH U1219, Bordeaux, F-33000, France.
Heliyon
|March 3, 2025
概括
深度学习模型使用谷歌地图数据自动化行人交叉路口安全评估. ConvNextV2显示出大规模道路安全审计的前景,改善行人保护.
科学领域:
- 计算机视觉 计算机视觉
- 道路安全工程 道路安全工程
- 城市规划 城市规划
背景情况:
- 步行者十字路口是车辆与行人事故的一个主要因素.
- 法国政府的举措旨在提高行人安全.
- 需要有效的,大规模的行人过道评估方法.
研究的目的:
- 通过使用深度学习,自动评估行人交叉路口和圆形路径.
- 利用空中和街道层面的图像进行全面的安全分析.
- 识别和分析与行人过道相关的风险因素.
主要方法:
- 使用的深度学习模型:convnextv2,resnet50,resnext50.使用的深度学习模型:convnextv2,resnet50,resnext50.
- 雇员面具R-CNN用于精确的细分和检测十字路口和圆圈.
- 分析了来自谷歌地图和谷歌街景的数据,跨越不同的法国设置.
主要成果:
- ConvNextV2在评估行人交叉路口风险因素方面表现优异.
- 自动化方法克服了传统的数据注释挑战.
- 确定了影响过道行人安全的关键风险因素.
结论:
- 深度学习为行人交叉路口审计提供了可扩展和客观的方法.
- 基于图像的自动化分析可以显著改善道路安全干预措施.
- 为了提高准确性,需要进一步增强数据集和改进方法.
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