Seo, Hyungjoon, Shi, Yunfan and Fu, Lang
(2024)
Automatic Damage Detection of Pavement through DarkNet Analysis of Digital, Infrared, and Multi-Spectral Dynamic Imaging Images.
Sensors (Basel), 24 (2).
464-.
Abstract
It is important to maintain the safety of road driving by automatically performing a series of processes to automatically measure and repair damage to the road pavement. However, road pavements include not only damages such as longitudinal cracks, transverse cracks, alligator cracks, and potholes, but also various elements such as manholes, road marks, oil marks, shadows, and joints. Therefore, in order to separate categories that exist in various road pavements, in this paper, 13,500 digital, IR, and MSX images were collected and nine categories were automatically classified by DarkNet. The DarkNet classification accuracies of digital images, IR images, and MSX images are 97.4%, 80.1%, and 91.1%, respectively. The MSX image is a enhanced image of the IR image and showed an average of 6% lower accuracy than the digital image but an average of 11% higher accuracy than the IR image. Therefore, MSX images can play a complementary role if DarkNet classification is performed together with digital images. In this paper, a method for detecting the directionality of each crack through a two-dimensional wavelet transform is presented, and this result can contribute to future research on detecting cracks in pavements.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | automatic damage detection, DarkNet, IR image, MSX image, pavement, wavelet transform |
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 25 Jan 2024 09:24 |
| Last Modified: | 27 Jan 2024 02:02 |
| DOI: | 10.3390/s24020464 |
| Open Access URL: | https://doi.org/10.3390/s24020464 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3178007 |
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