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Open Access

ARTICLE

VitSeg-Det & TransTra-Count: Networks for Robust Crack Detection and Measurement in Dynamic Video Scenes

Langyue Zhao1,2, Yubin Yuan3,*, Yiquan Wu2,*
1 College of Computer Science, Weinan Normal University, Weinan, 714000, China
2 College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China
3 College of Information Engineering, Yangzhou University, Yangzhou, 225127, China
* Corresponding Author: Yubin Yuan. Email: email; Yiquan Wu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070563

Received 18 July 2025; Accepted 22 October 2025; Published online 04 January 2026

Abstract

Regular detection of pavement cracks is essential for infrastructure maintenance. However, existing methods often ignore the challenges such as the continuous evolution of crack features between video frames and the difficulty of defect quantification. To this end, this paper proposes an integrated framework for pavement crack detection, segmentation, tracking and counting based on Transformer. Firstly, we design the VitSeg-Det network, which is an integrated detection and segmentation network that can accurately locate and segment tiny cracks in complex scenes. Second, the TransTra-Count system is developed to automatically count the number of defects by combining defect tracking with width estimation. Finally, we conduct experimental verification on three datasets. The results show that the proposed method is superior to the existing deep learning methods in detection accuracy. In addition, the actual scene video test shows that the framework can accurately label the defect location and output the number of defects in real time.

Keywords

Crack detection; multi object tracking; semantic segmentation; counting; transformer

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