{"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-damage-detection-and-classification-in","title":"Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN","arxiv_id":"1811.04535","date":"2018-11-12","proceeding":null,"authors":["Janpreet Singh","Shashank Shekhar"],"abstract":"This paper summarizes the design, experiments and results of our solution to\nthe Road Damage Detection and Classification Challenge held as part of the 2018\nIEEE International Conference On Big Data Cup. Automatic detection and\nclassification of damage in roads is an essential problem for multiple\napplications like maintenance and autonomous driving. We demonstrate that\nconvolutional neural net based instance detection and classfication approaches\ncan be used to solve this problem. In particular we show that Mask-RCNN, one of\nthe state-of-the-art algorithms for object detection, localization and instance\nsegmentation of natural images, can be used to perform this task in a fast\nmanner with effective results. We achieve a mean F1 score of 0.528 at an IoU of\n50% on the task of detection and classification of different types of damages\nin real-world road images acquired using a smartphone camera and our average\ninference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti\ngraphic card. The code and saved models for our approach can be found here :\nhttps://github.com/sshkhr/BigDataCup18 Submission","url_abs":"http://arxiv.org/abs/1811.04535v1","url_pdf":"http://arxiv.org/pdf/1811.04535v1.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-damage-detection-and-classification-in","repo_url":"https://github.com/sshkhr/BigDataCup18_Submission","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"road-damage-detection","task_name":"Road Damage Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"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}