Joint semantic segmentation of road objects and lanes using Convolutional Neural Networks

This paper presents a multi-task instance segmentation neural network able to provide both road lane and road participants detection. The multi-task approach, ERFNet-based, allows feature sharing and reduces the computational requirements of the overall detection architecture, allowing real time performance even in configurations with limited hardware. The proposed method includes an ad-hoc training procedure and automatic dataset creation mechanism that is also introduced in this paper. The proposed solution has been tested and validated through a newly generated public dataset derived from the BDD100K of 19K images, and in real scenarios. The results obtained prove the viability of the work for road application and its real time performance.

Journal article

Robotics and Autonomous Systems