MAFNet: Segmentation of Road Potholes With Multimodal Attention Fusion Network for Autonomous Vehicles

Zhen Feng, Yanning Guo, Qing Liang, M. Usman Maqbool Bhutta, Hengli Wang, Ming Liu, Yuxiang Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Road potholes can cause discomforts to passengers and even traffic accidents to vehicles. Accurate segmentation of road potholes is an important capability for autonomous vehicles to ensure safe driving. Some methods on road-pothole segmentation use single-modal data (i.e., RGB images). The main challenge faced by these methods is that the visual appearance of road potholes is often close to road areas, making these networks difficult to distinguish them. Recent methods resort to fusing RGB images with depth/disparity images for pothole segmentation. However, their performance is still not satisfactory in real-world applications. To achieve superior results, this article proposes a novel data fusion network for road-pothole segmentation, where a channel attention fusion module and a dual attention fusion (DAF) module are designed to hierarchically fuse the RGB and disparity data. We evaluate our proposed network using a public dataset, and the experimental results demonstrate the superiority over the state-of-the-art networks.

Original languageEnglish
Article number3523712
JournalIEEE Transactions on Instrumentation and Measurement
Volume71
DOIs
Publication statusPublished - 2022

Keywords

  • Attention mechanism
  • autonomous vehicles
  • RGB-disparity fusion
  • road potholes
  • semantic segmentation

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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