论文标题
使用卷积神经网络对多级图像集标签和道路条件分类的近实时地图构建
Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks
论文作者
论文摘要
天气是影响运输和道路安全的重要因素。在本文中，我们利用最先进的卷积神经网络来标记位于北美各地的Street和Highway摄像机拍摄的图像。在具有多个深度学习框架的实验中使用了路相机快照，以按道路条件对图像进行分类。这些实验的训练数据使用的图像标记为干，湿，冰/冰，贫穷和离线。该实验测试了六个卷积神经网络（VGG-16，Resnet50，Xception，InceptionResnetv2，EfficityNet-B0和EfficityNet-B4）的不同配置，以评估其对此问题的适合性。测量每个框架配置的精度，准确性和召回率。此外，训练组的总体规模和各个类别的大小都不同。最后的训练集包括使用上述五个类标记的47,000张图像。发现有效网络-B4框架最适合此问题，尽管有效网络B0在执行时间的一半时达到了90.3％的验证精度，但精度达到了90.3％。据观察，在整个项目中，VGG-16具有转移学习的VGG-16对于具有有限的硬件资源的伪标签非常有用。然后将有效网络-B4框架放入实时生产环境中，在该环境中可以持续实时对图像进行分类。然后，使用分类图像构建一张地图，显示北美各个相机位置的实时道路条件。这些框架的选择和我们的分析考虑了实时地图构建功能的独特要求。本文还提供了对使用这些框架的半自动数据集标记过程的详细分析。
Weather is an important factor affecting transportation and road safety. In this paper, we leverage state-of-the-art convolutional neural networks in labelling images taken by street and highway cameras located across across North America. Road camera snapshots were used in experiments with multiple deep learning frameworks to classify images by road condition. The training data for these experiments used images labelled as dry, wet, snow/ice, poor, and offline. The experiments tested different configurations of six convolutional neural networks (VGG-16, ResNet50, Xception, InceptionResNetV2, EfficientNet-B0 and EfficientNet-B4) to assess their suitability to this problem. The precision, accuracy, and recall were measured for each framework configuration. In addition, the training sets were varied both in overall size and by size of individual classes. The final training set included 47,000 images labelled using the five aforementioned classes. The EfficientNet-B4 framework was found to be most suitable to this problem, achieving validation accuracy of 90.6%, although EfficientNet-B0 achieved an accuracy of 90.3% with half the execution time. It was observed that VGG-16 with transfer learning proved to be very useful for data acquisition and pseudo-labelling with limited hardware resources, throughout this project. The EfficientNet-B4 framework was then placed into a real-time production environment, where images could be classified in real-time on an ongoing basis. The classified images were then used to construct a map showing real-time road conditions at various camera locations across North America. The choice of these frameworks and our analysis take into account unique requirements of real-time map building functions. A detailed analysis of the process of semi-automated dataset labelling using these frameworks is also presented in this paper.
