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TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
March 26, 2024, 4:48 a.m. | Quang-Huy Che, Duc-Tri Le, Minh-Quan Pham, Vinh-Tiep Nguyen, Duc-Khai Lam
cs.CV updates on arXiv.org arxiv.org
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$^+_{\text{Large}}$ …
abstract accuracy adept art arxiv autonomous autonomous driving computational costs cs.cv current driving efficiency navigation paper real-time safety segmentation semantic sota state type
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