| 常 丽,张 雪,蒋 辉,杨 娟,万紫玉.融合 YOLOv5s 与 SRGAN 的实时隧道火灾检测[J].电子测量与仪器学报,2022,36(8):223-230 |
| 融合 YOLOv5s 与 SRGAN 的实时隧道火灾检测 |
| Real-time tunnel fire detection by fusion of YOLOv5s and SRGAN |
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| DOI： |
| 中文关键词: 火焰检测 小目标检测 YOLOv5s 注意力机制 SRGAN |
| 英文关键词:flame detection small target detection YOLOv5s attention mechanism SRGAN |
| 基金项目:沈阳市科技项目（20 206 4 21）资助 |
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| 中文摘要: |
| 针对传统隧道火灾检测方法速度慢、误检率高的问题,提出了一种基于 YOLOv5s 的实时火焰检测算法,采用 K-means
重新计算锚框尺寸。 本文提出的 YOLOv5s-SRGAN 融合算法,在 1 326 幅隧道火焰图像中的召回率为 94%,是 YOLOv5s 的 1. 7
倍。 引入了 CBAM 注意力机制模块和梯度均衡机制,分别通过特征提取网络和损失函数提升模型的性能。 与原 YOLOv5s 相
比,火焰检测的平均正确率(IOU= 0. 5)提高了 44%,测试集平均检测速度为 32 FPS。 结果表明,改进后的火焰检测算法对小火
焰目标有了更好的识别效果。 |
| 英文摘要: |
| Aiming at the problems of slow speed and high false detection rate of traditional tunnel fire detection methods, a real-time
flame detection algorithm based on YOLOv5s was proposed, the size of anchorage frame was recalculated by K-means. In this paper, a
fusion algorithm of YOLOv5s-SRGAN is proposed. The recall rate of 1 326 tunnel flame images is 94%, 1. 7 times that of YOLOv5s.
CBAM attention mechanism module and gradient equalization mechanism were introduced to improve the performance of the model
through feature extraction network and loss function respectively. Compared with YOLOv5s, the average accuracy of flame detection
(IOU= 0. 5) is increased by 44%, the average detection speed of the test set reached 32 FPS. The results show that the improved flame
detection algorithm has better recognition effect on small flame targets. |
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