Channel Attention in LiDAR-camera Fusion for Lane Line Segmentation

Abstract

To assess the contributions of the different feature channels of sensors, we introduce a novel multimodal fusion method and demonstrate its practical utility using LiDAR-camera fusion networks. Specifically, a channel attention module that can be easily added to a fusion segmentation network is proposed. In this module, we use the channel attention mechanism to obtain the cross-channel local interaction information, and the weights of feature channels are assigned to represent the contributions of different feature channels. To verify the effectiveness of the proposed method, we conduct experiments on two types of feature fusion with the KITTI benchmark and A2D2 dataset. Our model achieves precise edge segmentation, with a 5.59% gain in precision and a 2.12% gain in F2-score compared to the values of the original fusion method. We believe that we have introduced a new optimization idea for multimodal fusion.