{"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/shrec-2022-pothole-and-crack-detection-in-the","title":"SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data","arxiv_id":"2205.13326","date":"2022-05-26","proceeding":null,"authors":["Elia Moscoso Thompson","Andrea Ranieri","Silvia Biasotti","Miguel Chicchon","Ivan Sipiran","Minh-Khoi Pham","Thang-Long Nguyen-Ho","Hai-Dang Nguyen","Minh-Triet Tran"],"abstract":"This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/mask pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.","url_abs":"https://arxiv.org/abs/2205.13326v5","url_pdf":"https://arxiv.org/pdf/2205.13326v5.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":"shrec-2022-pothole-and-crack-detection-in-the","repo_url":"https://gitlab.com/4ndr3aR/pothole-mix-segmentation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"pothole-mix","name":"Pothole Mix","full_name":"Pothole Mix Semantic Segmentation Dataset for Road Damage Detection and Segmentation"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"Baseline - DeepLabv3+","rank_in_archive_order":1,"of":7,"metrics":{"Test Dice Multiclass":"0 .789","Test mIoU":"0 .676","Validation Dice Multiclass":"0 .814","Validation mIoU":"0 .711"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"PUCP-MAnet","rank_in_archive_order":2,"of":7,"metrics":{"Test Dice Multiclass":"0 .827","Test mIoU":"0 .725","Validation Dice Multiclass":"0 .810","Validation mIoU":"0 .705"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"PUCP-Unet","rank_in_archive_order":3,"of":7,"metrics":{"Test Dice Multiclass":"0 .824","Test mIoU":"0 .720","Validation Dice Multiclass":"0 .804","Validation mIoU":"0 .698"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"PUCP-Unet++","rank_in_archive_order":4,"of":7,"metrics":{"Test Dice Multiclass":"0 .832","Test mIoU":"0 .731","Validation Dice Multiclass":"0 .800","Validation mIoU":"0 .694"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"HCMUS-SegFormer","rank_in_archive_order":5,"of":7,"metrics":{"Test Dice Multiclass":"0 .747","Test mIoU":"0 .628","Validation Dice Multiclass":"0 .637","Validation mIoU":"0 .523"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"HCMUS-DeepLabv3+","rank_in_archive_order":6,"of":7,"metrics":{"Test Dice Multiclass":"0 .823","Test mIoU":"0 .719","Validation Dice Multiclass":"0 .802","Validation mIoU":"0 .695"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pothole-mix","task":"Semantic Segmentation","dataset":"Pothole Mix","model":"HCMUS-CPS-DLU-Net","rank_in_archive_order":7,"of":7,"metrics":{"Test Dice Multiclass":"0 .789","Test mIoU":"0 .677","Validation Dice Multiclass":"0 .763","Validation mIoU":"0 .647"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}