基于计算机视觉的自动驾驶者头盔违规检测和在印度智能城市场景中使用NVIDIA TAO工具包和YOLOv8的车辆识别
Uttam U Deshpande1, Goh Kah Ong Michael2, Sufola Das Chagas Silva Araujo3
1Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Karnataka, India.
Frontiers in artificial intelligence
|August 6, 2025
概括
这项研究引入了一种两步深度学习系统,用于检测两轮车交通违规行为,特别是无头盔驾驶和车牌识别. 这种先进的模型实现了高精度,有助于实时执行交通法规,并提高了道路安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 道路安全工程 道路安全工程
背景情况:
- 两轮车交通违规行为,包括无头盔骑行和超载,对印度的公共安全构成重大风险.
- 传统的交通监控方法不足以有效识别和执行这些违规行为.
- 深度学习为自动检测和执行交通规则提供了有希望的解决方案.
研究的目的:
- 开发和评估一个两步深度学习模型,用于实时检测两轮车驾驶员,头盔佩戴状态和车牌识别.
- 通过创建定制数据集来解决有限的公开可用的交通数据集的挑战.
- 为了改善交通法规的执行,减少头盔违法行为.
主要方法:
- 实施了两步的深度学习方法,利用NVIDIA的DetectNet (ResNet18 CNN) 进行初始的骑手检测.
- 第二阶段使用YOLOv8实时检测头盔和光学字符识别 (OCR) 来提取车牌.
- 为模型培训和验证,创建了一个自定义的摩托车骑手图像数据集.
主要成果:
- 拟议的模型在头盔检测中实现了98.56%的准确性.
- 该系统在检测没有戴头盔的骑手的车牌时显示了97.6%的准确性.
- 实验分析证实了该模型在识别关键交通违规行为的有效性.
结论:
- 开发的深度学习系统为实时识别两轮车交通违规提供了有效的解决方案.
- 该研究强调了人工智能在提高道路安全和执行交通法规方面的潜力.
- 拟议的模型可以有助于减少与头盔有关的事故,并改善整体交通合规性.
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