Citation

Qiao JJ, Wu X, He JY, Li W, Peng Q. IEEE Trans. Intel. Transp. Syst. 2022; 23(4): 3012-3025.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3029006

PMID

unavailable

Abstract

Adverse weather conditions seriously threaten the traffic safety, especially for rainy days with the ponding water on the road surface, which potentially result in vehicle crashes, person injuries and crash fatalities. Automatic splashed water detection based on surveillance videos is an attractive way to effectively prevent the traffic accidents. However, surveillance videos exhibit great variations with lighting changes, illumination conditions and complex backgrounds, which pose great difficulties in automatic recognition. In this paper, a novel deep learning based approach is proposed to detect the splashed water. To the best of our knowledge, this is the first work on this topic based on deep learning. An effective semantic segmentation network, called SWNet, is novelly proposed to extract the potential splashed water regions. An encoder-decoder structure is designed to capture the visual characteristics of splashed water. SWNet achieves high efficiency by reusing pooling indices and adopting the light-weight decoder. With the multi-scale feature fusion structure, SWNet integrates the coarse semantic information and detailed appearance information, which significantly boosts the accuracy and refines the edge segmentation. A weighted cross entropy loss for splashed water is adopted to cope with the unbalanced distribution between splashed water and backgrounds. Moreover, a splashed water attention module is designed to focus on the salient regions of moving vehicles and splashed water, by performing attention mechanism to integrate global contextual information in semantic segmentation. Experiments conducted on a newly collected splashed water dataset demonstrate the effectiveness and efficiency of the proposed approach, which outperforms the state-of-the-art methods.

Language: en

Keywords

Accidents; deep learning; Deep learning; Image segmentation; intelligent transportation system; Meteorology; Roads; semantic segmentation; Semantics; Splashed water detection; Water