{"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/bowfire-detection-of-fire-in-still-images-by","title":"BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis","arxiv_id":"1506.03495","date":"2015-06-10","proceeding":null,"authors":["Daniel Y. T. Chino","Letricia P. S. Avalhais","Jose F. Rodrigues Jr.","Agma J. M. Traina"],"abstract":"Emergency events involving fire are potentially harmful, demanding a fast and\nprecise decision making. The use of crowdsourcing image and videos on crisis\nmanagement systems can aid in these situations by providing more information\nthan verbal/textual descriptions. Due to the usual high volume of data,\nautomatic solutions need to discard non-relevant content without losing\nrelevant information. There are several methods for fire detection on video\nusing color-based models. However, they are not adequate for still image\nprocessing, because they can suffer on high false-positive results. These\nmethods also suffer from parameters with little physical meaning, which makes\nfine tuning a difficult task. In this context, we propose a novel fire\ndetection method for still images that uses classification based on color\nfeatures combined with texture classification on superpixel regions. Our method\nuses a reduced number of parameters if compared to previous works, easing the\nprocess of fine tuning the method. Results show the effectiveness of our method\nof reducing false-positives while its precision remains compatible with the\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1506.03495v1","url_pdf":"http://arxiv.org/pdf/1506.03495v1.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":"bowfire-detection-of-fire-in-still-images-by","repo_url":"https://github.com/xiaoyihan6/ms-fsdb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"bowfire-detection-of-fire-in-still-images-by","repo_url":"https://github.com/Lukeli0425/Fire-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"bowfire-detection-of-fire-in-still-images-by","repo_url":"https://github.com/securade/hub","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fire-detection","task_name":"Fire Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"management","task_name":"Management"},{"task_slug":"texture-classification","task_name":"Texture Classification"}],"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}