U19-Net: a deep learning approach for obstacle detection in self-driving cars

Abstract

Development of self-driving cars aims to drive safely from one point to another in a coordinated system where an on-board system should react and possibly alert drivers about the driving environments and possible collisions that may arise between drivers and obstacles. There are many deep learning approaches available for obstacle detection especially convolutional neural networks (CNNs) with improvement accuracy, and encoder–decoder networks are CNNs with a current attraction for researchers mainly because these models provide better results than classical statistical models for image segmentation and object classification tasks. This work proposes U19-Net an encoder–decoder deep model that explores the deep layers of a VGG19 model as an encoder following a symmetrical approach with an U-Net decoder designed for pixel-wise classifications. The U19-Net has end-to-end learning successfully effectiveness for the vehicle and pedestrian detection within the open-source Udacity dataset showing an IoU score of 87.08 and 78.18%, respectively. The proposed U19-Net is compared with five recent CNN networks using the AP metric, obtaining near results (less than 5%) for the faster R-CNN, one of the most commonly used networks for object detection.