基于改进YOLOv5s算法的隧道初期火灾检测模型

Optimization method for tunnel initial fire detection based on YOLOv5s algorithm

为提升公路隧道初期火灾的检出率与检测精度,考虑初期烟火特征量小且不易侦测的特点,提出一种基于改进YOLOv5s算法的公路隧道初期火灾检测模型。首先,在YOLOv5s特征检测层并入变压器预测头,在原有3个特征检测头的基础上新增第4个160×160尺度的特征检测头,以增强多尺度识别能力;同时,引入加权双向特征金字塔网络(BiFPN)结构,用于融合高低层火焰和烟雾的语义信息;然后,采用完全交并比(CIoU)替换距离交并比(DIoU),并在置信度损失中采用Focal Loss改进YOLOv5s的损失函数,从而提升新模型整体的训练效果和检测精确度;最后,在真实隧道内开展初期火灾模拟试验,获取50000幅训练集样本,并结合2022年3月1日江苏镇江观音山隧道真实火灾视频数据,对比分析YOLOv5s-Opt和YOLOv5s算法模型。结果表明:YOLOv5s-Opt对初期火灾的平均检测精度达到90.38%,比YOLOv5s提高2.06%;对于同一段火灾实测视频,YOLOv5s-Opt的检出率比YOLOv5s高出3.63%。YOLOv5s-Opt算法模型更擅长初期火灾小目标的检测和识别,在检测精度和检出率方面更具有优势,检测效果明显,满足实际火灾检测需要。

To improve the detection rate and accuracy of initial fire in highway tunnel,considering the characteristics of the small amount of initial fireworks and difficult detection,an improved fire detection method based on YOLOv5s was proposed.The"transformer prediction heads"was incorporated into the YOLOv5s feature detection layer,and a fourth 160×160 scale feature detection head was added on the basis of the original three feature detection heads to enhance the multi-scale recognition capability.Meanwhile,the bidirectional feature pyramid bidirectional feature pyramid network(BiFPN)structure was introduced to blend the semantic information of high and low layers of flame and smoke.Complete intersection over union(CIoU)was used to replace distance intersection over union(DIoU),and focal loss was used in confidence loss to improve YOLOv5s loss function,so as to improve the overall training effect and detection accuracy of the new model.The experiment was carried out to simulate the initial fire in a real tunnel,and 50000 training set samples were obtained.Combined with the video data of the real fire in Guyinshan tunnel in Zhenjiang,Jiangsu province on March 1,2022,the YOLOv5s-Opt and YOLOv5s algorithm models were compared and analyzed.The results show that the average detection accuracy of YOLOv5s-Opt is 90.38%,which is 2.06%higher than that of YOLOv5s.For the same fire measurement video,the detection rate of YOLOv5s-Opt is 3.63%higher than that of YOLOv5s.The YOLOv5s-Opt algorithm model performs better at the detection and identification of small fire targets in the initial stage,and has advantages in detection accuracy and detection rate,and the detection effect is obvious,which can meet the needs of actual fire detection.