🇭🇰 Aug 2022 | Paper related to road potholes segmentation is accepted in IEEE Transactions on Instrumentation & Measurement (IF: 5.3+).

Title: MAFNet: Segmentation of Road Potholes with Multi-modal Attention Fusion Network for Autonomous Vehicles.

Road Inspection Deep Learning
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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.

BibTeX

BibTex

@ARTICLE{9864311,
  author={Feng, Zhen and Guo, Yanning and Liang, Qing and Usman Maqbool Bhutta, M. and Wang, Hengli and Liu, Ming and Sun, Yuxiang},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={MAFNet: Segmentation of Road Potholes with Multi-modal Attention Fusion Network for Autonomous Vehicles}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TIM.2022.3200100}}