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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.16631 |
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| _version_ | 1866913559152164864 |
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| author | Han, Xiaoyi Pu, Nan Feng, Zunlei Bei, Yijun Zhang, Qifei Cheng, Lechao Xue, Liang |
| author_facet | Han, Xiaoyi Pu, Nan Feng, Zunlei Bei, Yijun Zhang, Qifei Cheng, Lechao Xue, Liang |
| contents | The current irregularities in existing public Fire and Smoke Detection (FSD) datasets have become a bottleneck in the advancement of FSD technology. Upon in-depth analysis, we identify the core issue as the lack of standardized dataset construction, uniform evaluation systems, and clear performance benchmarks. To address this issue and drive innovation in FSD technology, we systematically gather diverse resources from public sources to create a more comprehensive and refined FSD benchmark. Additionally, recognizing the inadequate coverage of existing dataset scenes, we strategically expand scenes, relabel, and standardize existing public FSD datasets to ensure accuracy and consistency. We aim to establish a standardized, realistic, unified, and efficient FSD research platform that mirrors real-life scenes closely. Through our efforts, we aim to provide robust support for the breakthrough and development of FSD technology. The project is available at \href{https://xiaoyihan6.github.io/FSD/}{https://xiaoyihan6.github.io/FSD/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Multi-Scene Fire and Smoke Detection Han, Xiaoyi Pu, Nan Feng, Zunlei Bei, Yijun Zhang, Qifei Cheng, Lechao Xue, Liang Computer Vision and Pattern Recognition The current irregularities in existing public Fire and Smoke Detection (FSD) datasets have become a bottleneck in the advancement of FSD technology. Upon in-depth analysis, we identify the core issue as the lack of standardized dataset construction, uniform evaluation systems, and clear performance benchmarks. To address this issue and drive innovation in FSD technology, we systematically gather diverse resources from public sources to create a more comprehensive and refined FSD benchmark. Additionally, recognizing the inadequate coverage of existing dataset scenes, we strategically expand scenes, relabel, and standardize existing public FSD datasets to ensure accuracy and consistency. We aim to establish a standardized, realistic, unified, and efficient FSD research platform that mirrors real-life scenes closely. Through our efforts, we aim to provide robust support for the breakthrough and development of FSD technology. The project is available at \href{https://xiaoyihan6.github.io/FSD/}{https://xiaoyihan6.github.io/FSD/}. |
| title | Benchmarking Multi-Scene Fire and Smoke Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.16631 |