TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
<|reference_start|>TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation: 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.<|reference_end|>
@article{che2024twinlitenetplus:,
    title={TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane
  Segmentation},
    author={Quang-Huy Che, Duc-Tri Le, Minh-Quan Pham, Vinh-Tiep Nguyen, Duc-Khai
  Lam},
    journal={arXiv preprint arXiv:2403.16958},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16958},
    primaryClass={cs.CV}
}