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Swin-YOLOv5:研究和应用基于YOLOv5的火灾和烟雾检测算法
Shangjie Ge Zhang1, Fengxi Zhang2, Yuyang Ding3
1College of Science, Northeast Forestry University, Harbin 150040, China.
Computational intelligence and neuroscience
|November 29, 2023
概括
斯温-YOLOv5模型通过增强特征提取和融合来改善火灾和烟雾检测. 这种先进的算法在现实场景中更准确,更快速地检测火灾和烟雾目标.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 消防安全工程 消防安全工程
背景情况:
- 准确的火灾和烟雾监测对于公共安全至关重要.
- 由于其卷积性质,YOLOv5算法在特征提取和集成方面存在局限性,影响检测准确性.
研究的目的:
- 提出一个改进的目标检测算法Swin-YOLOv5,解决YOLOv5在火灾和烟雾检测方面的局限性.
- 为了增强YOLOv5模型的感受场,特征提取和特征融合能力.
主要方法:
- 将Swin转换机制集成到YOLOv5网络架构中.
- 在特征融合层网络中改进了特征拼接方法,采用加权特征拼接.
主要成果:
- 与原始YOLOv5.5相比,Swin-YOLOv5显示了mAP@0.5的0.7%增加和mAP@0.5:0.95的4.5%增加.
- 在高精度的检测速度上实现了1.8 FPS的增加.
- 改进的算法在检测错过或被原始算法检测不准确的目标方面显示出更高的准确性.
结论:
- 与标准YOLOv5.5相比,Swin-YOLOv5型号在火灾和烟雾检测方面提供了卓越的性能.
- 增强的特征提取和融合技术有助于提高准确性和速度,证明了适应现实场景检测的适应性.
- 这项工作为在各种环境中检测火烟提供了一种可行的方法,并提高了YOLOv5的特征处理能力.
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