Shen, X; Lu, Z; Zhang, Y; Xue, J; (2021) Lane line detection based on parallel spatial separation convolution. In: Proceedings of the the British Machine Vision Conference (BMVC) 2021. (pp. pp. 1-13). The BMVC

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Abstract

One of the fundamental tasks in autonomous driving is lane line detection. We aim to improve detection accuracy in complex scenarios and strike a better balance between performance and complexity of lane line detection networks. To this end, we first propose Parallel Spatial Separation Convolution (PSS-conv), a new convolution operation built on a new parallel spatial convolution decomposition and a channel-weighted feature merging strategy, to aggregate the features obtained from decomposed convolution. Then, we propose Parallel Spatial Separation Convolution with Message-Passing (PSS-conv-MP), in which a new message passing module is added before feature merging to enable sliceby-slice information propagation. Based on the PSS-conv, PSS-conv-MP, residual connection and non-bottleneck design, we construct a new lane line detection network called Parallel Spatial Separation Network (PSSNet), which can handle challenging scenes like curve and obscured lane lines. Extensive experiments show that PSSNet can achieve a superior performance on the challenging lane line detection benchmark CULane.

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