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

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Bibliographic Details
Title: VitSeg-Det & TransTra-Count: Networks for Robust Crack Detection and Measurement in Dynamic Video Scenes.
Authors: Zhao, Langyue, Yuan, Yubin, Wu, Yiquan
Source: Computers, Materials & Continua; 2026, Vol. 87 Issue 1, p1-31, 31p
Subject Terms: DEFECT tracking (Computer software development), AUTOMATIC tracking, COUNTING, INFRASTRUCTURE (Economics), COMPUTER vision, TRANSFORMER models, IMAGE segmentation, DEEP learning
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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
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. [ABSTRACT FROM AUTHOR]
ISSN:15462218
DOI:10.32604/cmc.2025.070563