{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/road-crack-detection-using-deep-convolutional","title":"Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding","arxiv_id":"1904.08582","date":"2019-04-18","proceeding":null,"authors":["Rui Fan","Mohammud Junaid Bocus","Yilong Zhu","Jianhao Jiao","Li Wang","Fulong Ma","Shanshan Cheng","Ming Liu"],"abstract":"Crack is one of the most common road distresses which may pose road safety\nhazards. Generally, crack detection is performed by either certified inspectors\nor structural engineers. This task is, however, time-consuming, subjective and\nlabor-intensive. In this paper, we propose a novel road crack detection\nalgorithm based on deep learning and adaptive image segmentation. Firstly, a\ndeep convolutional neural network is trained to determine whether an image\ncontains cracks or not. The images containing cracks are then smoothed using\nbilateral filtering, which greatly minimizes the number of noisy pixels.\nFinally, we utilize an adaptive thresholding method to extract the cracks from\nroad surface. The experimental results illustrate that our network can classify\nimages with an accuracy of 99.92%, and the cracks can be successfully extracted\nfrom the images using our proposed thresholding algorithm.","url_abs":"http://arxiv.org/abs/1904.08582v1","url_pdf":"http://arxiv.org/pdf/1904.08582v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"road-crack-detection-using-deep-convolutional","repo_url":"https://github.com/ruirangerfan/road_crack_detection_net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}