{"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/langgas-introducing-language-in-selective","title":"LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset","arxiv_id":"2503.02910","date":"2025-03-04","proceeding":null,"authors":["Wenqi Guo","Yiyang Du","Shan Du"],"abstract":"Gas leakage poses a significant hazard that requires prevention. Traditionally, human inspection has been used for detection, a slow and labour-intensive process. Recent research has applied machine learning techniques to this problem, yet there remains a shortage of high-quality, publicly available datasets. This paper introduces a synthetic dataset featuring diverse backgrounds, interfering foreground objects, diverse leak locations, and precise segmentation ground truth. We propose a zero-shot method that combines background subtraction, zero-shot object detection, filtering, and segmentation to leverage this dataset. Experimental results indicate that our approach significantly outperforms baseline methods based solely on background subtraction and zero-shot object detection with segmentation, reaching an IoU of 69\\% overall. We also present an analysis of various prompt configurations and threshold settings to provide deeper insights into the performance of our method. The code and dataset will be released after publication.","url_abs":"https://arxiv.org/abs/2503.02910v1","url_pdf":"https://arxiv.org/pdf/2503.02910v1.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":"langgas-introducing-language-in-selective","repo_url":"https://github.com/weathon/Lang-Gas","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"zero-shot-object-detection","task_name":"Zero-Shot Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"simgas","name":"SimGas","full_name":"Computer Simulated Gas Leakage Segmentation"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-simgas","task":"Classification","dataset":"SimGas","model":"LangGas","rank_in_archive_order":1,"of":1,"metrics":{"Frame Level Accuracy":"0.89"},"uses_additional_data":false},{"leaderboard":"/sota/segmentation-on-simgas","task":"Segmentation","dataset":"SimGas","model":"LangGas","rank_in_archive_order":1,"of":1,"metrics":{"IoU":"0.69","Precision":"0.82","Recall":"0.82"},"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}