Enhanced lane line detection algorithm for curves based on Resa-CC

doi:10.13474/j.cnki.11-2246.2023.0354

Abstract

Abstract: A curve enhanced lane detection algorithm based on cyclic feature fusion Resa-CC is proposed to address the issue of reduced accuracy in curve recognition caused by excessive curvature at road turns. This algorithm utilizes the shape priors of lane lines to capture the spatial relationships between rows and columns in image pixels, and fuse information to generate feature maps. The residual network is used as the main framework, and the encoder, decoder and attention mechanism modules are added. The Loss function adds curve structure constraints to improve the recognition accuracy of lane curves. The addition of the cyclic feature fusion module and the self attention mechanism module improved the accuracy by 3.41% and 1.1%, respectively, proving the effectiveness of the two modules. The accuracy of the Resa-CC algorithm can reach 96.83%, with an FPS of 35.68. The false detection rate FP and missed detection rate FN are 0.0315 and 0.0282, respectively. This indicates that the algorithm has high detection performance and can more accurately infer the position of the lane line in the curve when vehicles are driving.

Key words: traffic engineering, lane line detection, recurrent feature aggregation, deep learning, automatic driving, ResNet

CLC Number:

P237

LU Weijia, LIU Zeshuai, PAN Yuheng, LI Guoyan, LI Huijie, CONG Jia. Enhanced lane line detection algorithm for curves based on Resa-CC[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 25-30.

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