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

Semantic segmentation is a common task in autonomous driving to understandthe surrounding environment. Driveable Area Segmentation and Lane Detection areparticularly important for safe and efficient navigation on the road. However,original semantic segmentation models are computationally expensive and requirehigh-end hardware, which is not feasible for embedded systems in autonomousvehicles. This paper proposes a lightweight model for the driveable area andlane line segmentation. TwinLiteNet is designed cheaply but achieves accurateand efficient segmentation results. We evaluate TwinLiteNet on the BDD100Kdataset and compare it with modern models. Experimental results show that ourTwinLiteNet performs similarly to existing approaches, requiring significantlyfewer computational resources. Specifically, TwinLiteNet achieves a mIoU scoreof 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection taskwith only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000.Furthermore, TwinLiteNet can run in real-time on embedded devices with limitedcomputing power, especially since it achieves 60FPS on Jetson Xavier NX, makingit an ideal solution for self-driving vehicles. Code is available:url{https://github.com/chequanghuy/TwinLiteNet}.