Autonomous driving is rapidly improving as companies and researchers are racing to create the perfect system. One of the uncertain environments which these systems face is when there is a construction site on the road. Road construction sites are dynamic and the components of the construction site can vary drastically from one site to another.
The goal of this thesis work was to explore approaches for vision-based detection of construction sites on roads using images taken from a single camera such as a dashboard camera. This would be helpful for autonomous driving and navigation solutions once the construction work has been identified and localized for making informed decisions.
Custom datasets have been created using existing NuScenes dataset as a starting point. Images containing road construction work from three cities - Boston, Helsinki and Singapore are included in the datasets. The two created datasets are targeted for image classification and object detection problems. Deep convolutional neural networks (CNN) based algorithms were tested on the datasets to classify and detect road construction sites. The final results of this work provides a proof of concept for detecting and localizing construction sites using a vision-based system with support for future expansion.
