Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks

Somin Park, Seongdeok Bang, Hongjo Kam, Hyoungkwan Kim

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Cracks cause deterioration of road performance and functional or structural failure if not managed in a timely manner. This paper proposes an automated crack detection method using a car black box camera to address this problem. The proposed method uses a deep learning model [i.e., convolutional neural network (CNN)] composed of segmentation and classification modules. The segmentation process is performed to extract only the road surface in order to remove elements that interfere with crack detection in the black box image. Then, cracks are detected through analysis of patch units within the extracted road surface. The proposed CNN architecture classifies the elements of the road surface into three categories (i.e., crack, road marking, and intact area) with 90.45% accuracy. The results of the proposed CNN architecture are better than those of previous studies.

Original languageEnglish
Article number04019017
JournalJournal of Computing in Civil Engineering
Volume33
Issue number3
DOIs
Publication statusPublished - 2019 May 1

Bibliographical note

Funding Information:
This study was supported by a grant (18CTAP-C133290-02) from the Infrastructure and Transportation Technology Promotion Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications

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