TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

TL;DR

TwinLiteNet is a lightweight, efficient semantic segmentation model designed for autonomous driving, achieving high accuracy and real-time performance on embedded systems with minimal computational resources.

Contribution

The paper introduces TwinLiteNet, a novel lightweight model that maintains high segmentation accuracy while significantly reducing computational requirements for self-driving car applications.

Findings

Achieves 91.3% mIoU for Drivable Area segmentation.

Runs at 415 FPS on GPU RTX A5000.

Operates at 60 FPS on Jetson Xavier NX.

Abstract

Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road. However, original semantic segmentation models are computationally expensive and require high-end hardware, which is not feasible for embedded systems in autonomous vehicles. This paper proposes a lightweight model for the driveable area and lane line segmentation. TwinLiteNet is designed cheaply but achieves accurate and efficient segmentation results. We evaluate TwinLiteNet on the BDD100K dataset and compare it with modern models. Experimental results show that our TwinLiteNet performs similarly to existing approaches, requiring significantly fewer computational resources. Specifically, TwinLiteNet achieves a mIoU score of 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection task with only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000. Furthermore, TwinLiteNet can run in real-time on embedded devices with limited computing power, especially since it achieves 60FPS on Jetson Xavier NX, making it an ideal solution for self-driving vehicles. Code is available: url{https://github.com/chequanghuy/TwinLiteNet}.