TwinLiteNetPlus: A Real-Time Multi-Task Segmentation Model for Autonomous Driving

Abstract: Semantic segmentation is crucial for autonomous driving, particularly for the tasks of Drivable Area and Lane Segmentation, ensuring safety and navigation. To address the high computational costs of current state-of-the-art (SOTA) models, this paper introduces TwinLiteNetPlus, a model capable of balancing efficiency and accuracy. TwinLiteNetPlus incorporates standard and depth-wise separable dilated convolutions, reducing complexity while maintaining high accuracy. It is available in four configurations, from the robust 1.94 million-parameter TwinLiteNetPlus_{Large} to the ultra-lightweight 34K-parameter TwinLiteNetPlus_{Nano}. Notably, TwinLiteNetPlus_{Large} attains a 92.9% mIoU (mean Intersection over Union) for Drivable Area Segmentation and a 34.2% IoU (Intersection over Union) for Lane Segmentation. These results achieve remarkable performance, surpassing current state-of-the-art models while only requiring 11 times fewer Floating Point Operations (FLOPs) for computation. Rigorously evaluated on various embedded devices, TwinLiteNetPlus demonstrates promising latency and power efficiency, underscoring its potential for real-world autonomous vehicle applications. The code is available on https://github.com/chequanghuy/TwinLiteNetPlus.