Segmentation of Road Negative Obstacles Based on Dual Semantic-Feature Complementary Fusion for Autonomous Driving
Zhen Feng, Yanning Guo, Yuxiang Sun
Abstract
Segmentation of road negative obstacles (i.e., potholes and cracks) is important to the safety of autonomous driving. Although existing RGB-D fusion networks could achieve acceptable performance, most of them only conduct binary segmentation for negative obstacles, which does not distinguish potholes and cracks. Moreover, their performance is susceptible to depth noises, in which case the fluctuations of depth data caused by the noises may make the networks mistakenly treat the area as a negative obstacle. To provide a solution to the above issues, we design a novel RGB-D semantic segmentation network with dual semantic-feature complementary fusion for road negative obstacle segmentation. We also re-label an RGB-D dataset for this task, which distinguishes road potholes and cracks as two different classes. Experimental results show that our network achieves state-of-the-art performance compared to existing well-known networks.