使用深度学习和2D/3D图像检测沿海地区的柔性路面表面裂
Carlos Sanchez1, Feng Wang1, Yongsheng Bai1
1Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究开发了一个YOLOv5模型,使用2D/3D图像来检测路面的困境,改善了运输机构的自动化分析. 该模型显示了一致的检测能力,有助于行道性能评估.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 交通运输机构分析路面表面的压力,以评估网络性能.
- 自动图像分析和检测算法用于提高效率,但由于不准确性,需要工程师验证.
- 人工智能和机器学习 (AI/ML),特别是深度学习,为更强大的路面应急检测提供了潜力.
研究的目的:
- 开发一个YOLOv5深度学习模型来检测和分类路面表面的痛苦.
- 创建一个数据集的2D/3D路面图像从墨西哥湾沿岸地区 (路易斯安那州,密西西比州,德克萨斯州).
- 评估模型在青路面上的背景噪声中检测裂的性能.
主要方法:
- 使用了2D/3D路面图像,分辨率为4096 × 2048.
- 使用边界框对九个紧急和非紧急对象类的注释图像.
- 实施了六个数据增强场景 (水平和垂直翻转) 来评估模型的稳定性.
主要成果:
- YOLOv5模型展示了对定义的路面困境的一致检测.
- 在超过工会门0.5 (mAP50) 的交叉点的最高平均平均精度在培训场景中从0.437到0.462不等.
- 这项研究为墨西哥湾附近的路面部分建立了一个有价值的数据库.
结论:
- 像YOLOv5这样的深度学习模型对于视觉路面困境识别是有效的.
- 开发的模型有望提高自动化路面状况评估的准确性和效率.
- 进一步的研究可以增强用于路面工程的AI / ML算法,特别是在具有挑战性的沿海环境中.
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