Dual-Task Learning for Real-Time Semantic Segmentation in Autonomous Driving

Drivable Area Segmentation and Lane Detection constitute crucial tasks for the Visual Perception system of an Autonomous Vehicle. The majority of the approaches dealing with these tasks are addressed as Semantic Segmentation problems using heavy deep learning models that become computationally expensive. In this paper, a dual-task lightweight model is proposed, which comprises a novel dual-task feature fusion mechanism allowing it to exploit global, high-level information while retaining useful low-level details for each task. This model excels not only in terms of accuracy but also achieves real-time performance by solving these two tasks in a multi-task fashion. Our comparative study which was conducted on the standard BDD100 K dataset shows that our proposed method compares favorably with the state-of-the-art offering an optimal trade-off between accuracy and efficiency. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/DUTH-VCG/Dual-task-learning</uri>