DeepAI AI Chat
Log In Sign Up

Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks

by   Sheela Ramanna, et al.
The University of Winnipeg

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 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.


page 2

page 8

page 13


Intelligent Pothole Detection and Road Condition Assessment

Poor road conditions are a public nuisance, causing passenger discomfort...

iLabel: Interactive Neural Scene Labelling

Joint representation of geometry, colour and semantics using a 3D neural...

Fusion of neural networks, for LIDAR-based evidential road mapping

LIDAR sensors are usually used to provide autonomous vehicles with 3D re...

LabelSens: Enabling Real-time Sensor Data Labelling at the point of Collection on Edge Computing

In recent years, machine learning has made leaps and bounds enabling app...

Slash or burn: Power line and vegetation classification for wildfire prevention

Electric utilities are struggling to manage increasing wildfire risk in ...

The Oxford Road Boundaries Dataset

In this paper we present the Oxford Road Boundaries Dataset, designed fo...

Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems

This paper proposes an approach that predicts the road course from camer...