Overheight vehicle collisions continuously pose a serious threat to transportation infrastructure and public safety. This study proposed a vision-based method for automatic vehicle height measurement using deep learning and view geometry. In this method, vehicle instances are first segmented from traffic surveillance video frames by exploiting mask region-based convolutional neural network (Mask R-CNN). Then, 3D bounding box on each vehicle instance is constructed using the obtained vehicle silhouette and three orthogonal vanishing points in the surveilled traffic scene. By doing so, the vertical edges of the constructed 3D bounding box are directly associated with the vehicle image height. Last, the vehicle's physical height is computed by referencing an object with a known height in the traffic scene using single view metrology. A field experiment was performed to evaluate the performance of the proposed method, leading to the mean and maximum errors of 3.6 and 6.6, 5.8 and 12.9, 4.4 and 8.1, and 9.2 and 18.5 cm for cars, buses, vans, and trucks, respectively. The experiment also demonstrated the ability of the method to overcome vehicle occlusion, shadow, and irregular appearance interferences in height estimation suffered by existing image-based methods. The results signified the potential of the proposed method for overheight vehicle detection and collision warning in real traffic settings.

中文翻译：

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车辆高度的自动视觉测量，以帮助使用深度学习和视图几何测量超高碰撞的风险

超高车辆碰撞不断对交通基础设施和公共安全构成严重威胁。本研究提出了一种使用深度学习和视图几何的基于视觉的自动车辆高度测量方法。在这种方法中，车辆实例首先通过利用基于掩模区域的卷积神经网络（Mask R-CNN）从交通监控视频帧中分割出来。然后，使用获得的车辆轮廓和监控交通场景中的三个正交消失点，在每个车辆实例上构建 3D 边界框。通过这样做，构建的 3D 边界框的垂直边缘直接与车辆图像高度相关联。最后，车辆的物理高度是通过使用单视图计量学参考交通场景中已知高度的物体来计算的。进行了现场实验以评估所提出方法的性能，导致汽车、公共汽车、货车和卡车的平均和最大误差分别为 3.6 和 6.6、5.8 和 12.9、4.4 和 8.1，以及 9.2 和 18.5 厘米. 实验还证明了该方法能够克服现有基于图像的方法在高度估计中遇到的车辆遮挡、阴影和不规则外观干扰。结果表明所提出的方法在实际交通环境中用于超高车辆检测和碰撞警告的潜力。实验还证明了该方法能够克服现有基于图像的方法在高度估计中遇到的车辆遮挡、阴影和不规则外观干扰。结果表明所提出的方法在实际交通环境中用于超高车辆检测和碰撞警告的潜力。实验还证明了该方法能够克服现有基于图像的方法在高度估计中遇到的车辆遮挡、阴影和不规则外观干扰。结果表明所提出的方法在实际交通环境中用于超高车辆检测和碰撞警告的潜力。