计算机科学
情态动词
分割
特征(语言学)
人工智能
GSM演进的增强数据速率
编码(集合论)
计算机视觉
传感器融合
图像分割
模式识别(心理学)
语言学
化学
哲学
集合(抽象数据类型)
高分子化学
程序设计语言
作者
Hongliang Ye,Jilin Mei,Yu Hu
标识
DOI:10.1109/iv55152.2023.10186731
摘要
Freespace detection is an important part of autonomous driving technology. Compared with structured on-road scenes, unstructured off-road scenes face more challenges. Multi-modal fusion method is a viable solution to these challenges. But existing fusion methods do not fully utilize the multi-modal features. In this paper, we propose an effective multi-modal network named M2F2-Net for freespace detection in unstructured off-road scenes. We propose a multi-modal feature fusion strategy named Multi-modal Cross Fusion (MCF). MCF module is simple but effective in fusing the features of RGB images and surface normal maps. Meanwhile, a multi-modal segmentation decoder module is designed to decouple the segmentation of two modalities, and it further helps the features of both modalities to be fully utilized. In order to solve the problem that the road edge is difficult to extract in the unstructured scenes, we also propose an edge segmentation decoder module. Extensive experiments show that our approach can lead to significant improvements, which brings 6.1% F1 and 10.8% IoU improvements. Our code will be available at https://github.com/yhl1010/M2F2-Net.
科研通智能强力驱动
Strongly Powered by AbleSci AI