VRU-Net: Convolutional Neural Networks-Based Detection of Vulnerable Road Users

Abdelhamid Mammeri, Abdul Jabbar Siddiqui, Yiheng Zhao

Abstract: Research work on object detection for transportation systems have made considerable progress owing to the effectiveness of deep convolutional neural networks. While much attention has been given to object detection for automated vehicles (AVs), the problem of detecting them at road intersections has been underexplored. Specifically, most research work in this area have, to some extent, ignored vulnerable road users (VRUs) such as persons using wheelchairs, mobility scooters, or strollers. In this work, we seek to fill the gap by proposing VRU-Net, a CNN-based model designed to detect VRUs at road intersections. VRU-Net first learns to predict a VRUMask representing grid-cells in an input image that are highly probable of containing VRUs of interest. Based on the predicted VRUMask, regions/cells of interest are extracted from the image/feature maps and fed into the further layers for classification. In this way, we greatly reduce the number of regions to process when compared