SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection

Abstract

Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction. However, the existing two-stream networks are only fused at a specific network layer, which requires a lot of manual attempts to set up. As the CNN goes deeper, the two modal features become more and more advanced and abstract, and the fusion occurs at the feature level with a large gap, which can easily hurt the performance. In this study, we propose a novel fusion architecture called skip-cross networks (SkipcrossNets), which combines adaptively LiDAR point clouds and camera images without being bound to a certain fusion epoch. Specifically, skip-cross connects each layer to each layer in a feed-forward manner, and for each layer, the feature maps of all previous layers are used as input and its own feature maps are used as input to all subsequent layers for the other modality, enhancing feature propagation and multi-modal features fusion. This strategy facilitates selection of the most similar feature layers from two data pipelines, providing a complementary effect for sparse point cloud features during fusion processes. The network is also divided into several blocks to reduce the complexity of feature fusion and the number of model parameters. The advantages of skip-cross fusion were demonstrated through application to the KITTI and A2D2 datasets, achieving a MaxF score of 96.85% on KITTI and an F1 score of 84.84% on A2D2. The model parameters required only 2.33 MB of memory at a speed of 68.24 FPS, which could be viable for mobile terminals and embedded devices