TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation

• URL: http://arxiv.org/abs/2403.16958v1
• Date: Mon, 25 Mar 2024 17:17:45 GMT
• Title: TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
• Abstract summary: This paper introduces TwinLiteNetPlus to address the high computational costs of current state-of-the-art (SOTA) models. TwinLiteNetPlus incorporates standard and depth-wise separable dilated convolutions, reducing complexity while maintaining high accuracy.
• Score: 1.1027204173383738
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic segmentation is crucial for autonomous driving, particularly for 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 (TwinLiteNet$^+$), a model adept at balancing efficiency and accuracy. TwinLiteNet$^+$ 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 TwinLiteNet$^+_{\text{Large}}$ to the ultra-compact 34K-parameter TwinLiteNet$^+_{\text{Nano}}$. Notably, TwinLiteNet$^+_{\text{Large}}$ attains a 92.9\% mIoU for Drivable Area Segmentation and a 34.2\% IoU for Lane Segmentation. These results notably outperform those of current SOTA models while requiring a computational cost that is approximately 11 times lower in terms of Floating Point Operations (FLOPs) compared to the existing SOTA model. Extensively tested on various embedded devices, TwinLiteNet$^+$ demonstrates promising latency and power efficiency, underscoring its suitability for real-world autonomous vehicle applications.

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