Fast and accurate semantic segmentation of road crack video in a complex dynamic environment

ABSTRACT

This paper proposes a fast and accurate semantic segmentation of road crack video in a complex dynamic environment. First, a fast key frame selection algorithm is designed by combining the interframe dissimilarity constrained video frame difference method (FDM) and shot interval sampling method (SISM). Second, the complex and dynamic characteristics of application scenarios are studied, and a high-precision crack semantic segmentation DBPNet containing a densely diverse branch module (DDBM) and diverse branch pyramid module (DBPM) is proposed, which can not only focus on the local aggregation of the same scale feature but also fuse and reconstruct long-distance related semantic features. Finally, a dynamic video data set of cracks in complex environments, including multiple types of interference, different weather and lighting is established, and experimental analysis is carried out. The results show that the proposed method can improve the efficiency of video crack detection by 5–7 times compared to the method without using key frames, and the detection accuracy can reach 72.85%, which can adapt to dynamic video changes in a variety of complex scenes. The proposed network, DBPNet, outperforms the current state-of-the-art methods in road crack semantic segmentation on challenging data sets in terms of both Dice and MIOU.

KEYWORDS:

• Road crack video
• complex dynamic environment
• semantic segmentation
• fast key frame selection algorithm
• DBPNet

Acknowledgments

The authors would like to thank the chief editor and anonymous reviewers for their constructive and valuable suggestions on the drafts of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Sichuan Science and Technology Program: [Grant Number 2022YFS0533]; the National Natural Science Foundation of China: [Grant Nos. 42271424 and 41871289]; Sichuan Transportation Science and Technology Program: [Grant Number 2021-B-02].