Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

Abstract

Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CNNs) have been proposed for pixel-wise drivable area and road anomaly detection. In addition, some benchmark datasets, such as KITTI and Cityscapes, have been widely used. However, the existing benchmarks are mostly designed for self-driving cars. There lacks a benchmark for ground mobile robots, such as robotic wheelchairs. Therefore, in this paper, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating the existing state-of-the-art single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features. Furthermore, we propose a novel module, referred to as the dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks to fuse different types of visual features effectively and efficiently. The experimental results show that the transformed disparity image is the most informative visual feature and the proposed DFM-RTFNet outperforms the state-of-the-arts. Additionally, our DFM-RTFNet achieves competitive performance on the KITTI road benchmark. Our benchmark is publicly available at https://sites.google.com/view/gmrb.

11 pages, 12 figures and 5 tables. This paper is accepted by IEEE T-Cyber

References in corpus (4)

• Fully Convolutional Networks for Semantic Segmentation
• SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
• Road Damage Detection Based on Unsupervised Disparity Map Segmentation
• Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation

Cited by in corpus (8)

• Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms
• PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching
• Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
• Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection
• Multi-Modal Multi-Task (3MT) Road Segmentation
• SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume
• Co-Teaching: An Ark to Unsupervised Stereo Matching
• S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs