Multi-frame Network Feature Fusion Model and Self-attention Mechanism for Vehicle Lane Line Detection

Guang Zhu ; Yajuan Liu ; Jiyue Wang

Computer Science and Information Systems, Tome 21 (2024) no. 4.

Résumé

The traditional lane detection networks mainly use independent single frame images to extract features first and then detect them, which cannot deal with the scene with complex background well. Therefore, this paper proposes a lane parallel detection network based on multi-frame network feature fusion model and self-attention mechanism according to the scene characteristics that vehicles can obtain continuous images during normal driving. Firstly, a parallel feature extraction structure is designed. On the one hand, a single frame network with high precision is used to extract the features of the current frame. On the other hand, a lightweight multi-frame network is designed to extract features of low-resolution multi-frame temporal images. And the recurrent neural network module is used to fuse the extracted temporal features and obtain multi-frame features. Self-attention mechanism can effectively capture the relevant information of internal features. Then the fusion module of single frame feature, multi-frame feature and self-attention feature is designed. The feature map of lane line is output by up-sampling network. The experimental results show that the network in this paper has significant improvement in both objective detection accuracy and subjective effect compared with other methods.

Détail

Keywords: vehicle lane detection, multi-frame network feature fusion, self-attention mechanism