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在新的开放火灾和烟雾数据集上对YOLO型探测器的比较性能评估
Constantin Catargiu1, Nicolae Cleju1, Iulian B Ciocoiu1
1Faculty of Electronics, Telecommunications and Information Technology, Gheorghe Asachi Technical University of Iasi, Bd. Carol I 11A, 700506 Iasi, Romania.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一个新的FireAndSmoke数据集包含超过22,000张图像,有助于火灾和烟雾检测研究. YOLO10模型的精度达到89%,在检测火灾和烟雾方面表现优于其他模型.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的火灾和烟雾检测对于公共安全和环境监测至关重要.
- 现有的数据集可能缺乏多样性或现实世界的复杂性,阻碍了模型的概括.
研究的目的:
- 介绍全面的FireAndSmoke开放数据集,用于训练和评估火灾/烟雾检测模型.
- 在这个新数据集上对各种YOLO型物体探测器的性能进行比较.
主要方法:
- 从各种来源 (YouTube,公共互联网) 编译了超过22,000张图像和93,000个实例.
- 包括各种场景:白天/夜晚,室内/室外,城市/森林,单个/多个实例,以及具有挑战性的案例.
- 应用严格的数据选择,预处理和标签,遵循FAIR原则.
- 通过使用精度,回忆,平均精度 (mAP) 和速度评估YOLO家族探测器.
主要成果:
- YOLO10模型表现出卓越的性能,达到89%的准确性.
- YOLO10记录了超过91%的平均精度 (mAP@50).
- 该数据集涵盖了广泛的现实世界火灾和烟雾条件.
结论:
- 消防和烟雾数据集为推进火灾和烟雾检测技术提供了宝贵的资源.
- YOLO10模型代表了对火灾和烟雾的物体检测准确度的显著改进.
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