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

• Published in: IEEE transactions on cybernetics Vol. 52; no. 10; pp. 10750 - 10760
• Main Authors: Wang, Hengli, Fan, Rui, Sun, Yuxiang, Liu, Ming
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Anomaly detection; Artificial neural networks; Autonomous cars; Benchmark testing; Benchmarks; Convolutional neural networks; Data integration; Deep learning; Deep learning in robotics and automation; dynamic fusion; Electromotive wheelchairs; Feature extraction; Image segmentation; Mobile robots; Modules; Roads; Robots; semantic scene understanding; Semantic segmentation; Semantics; State-of-the-art reviews; Wheelchairs
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2021.3064089

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 pixelwise 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 article, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating existing state-of-the-art (SOTA) 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 SOTAs. In addition, our DFM-RTFNet achieves competitive performance on the KITTI road benchmark.