| Citation: | HU D D，ZHANG Z T，NIU G C. Lane line detection incorporating CBAM mechanism and deformable convolutional network[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2150-2160 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0601
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In order to meet the accuracy and real-time requirements of autonomous driving and advanced driver assistance systems (ADAS) for lane line detection, a CADCN lane line detection method incorporating convolutional block attention module (CBAM) mechanism and deformable convolutional network (DCN) was proposed. Firstly, the CBAM mechanism was embedded in the feature extraction module to enhance the useful features and suppress the useless feature responses. Secondly, DCN was used to replace the conventional convolutional network, and the geometric deformation of lane lines was learned by sampling with offset to improve the modeling capability of the convolution kernel. Finally, based on the idea of row anchor classification, the location point along the row was selected and classified, so as to predict the lane line location information and thus improve the real-time performance of the lane line detection model. The CADCN model was trained and validated on the public lane line dataset. While ensuring real-time performance, the accuracy rate of the model on the TuSimple dataset reaches 96.63%, and the comprehensive evaluation index
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