基于改进YOLOv5的火焰烟雾检测

doi:10.19678/j.issn.1000-3428.0064509

摘要/Abstract

摘要： 为更好地实现基于图像的实时火灾预警，结合YOLOv5s提出一种改进的火焰烟雾检测算法。将YOLOv5s颈部原有的路径聚合网络模块替换为双向交叉尺度融合模块，使深层网络可以直接提取浅层特征，增强信息流并提升网络特征融合能力。在YOLOv5s头部添加引入协调注意力的推理层，在不过多增加计算量的前提下加强检测头对网络信息的提取和定位能力，并提高检测精度。采用HSV色域增强、随机旋转、Mosaic等多种数据增强技术调整并扩充训练数据，使用k-means聚类算法获取数据集先验锚框，增强检测模型鲁棒性。实验结果表明，与基于YOLOv5s的火焰烟雾检测算法相比，改进算法的平均精度均值提升了3.2个百分点，检测速度达到243帧/s，并且保持了YOLOv5s的轻量化优势，在遮挡、夜晚、小目标等复杂场景下均具有较好的火焰烟雾检测效果。

关键词: YOLOv5网络, 火焰烟雾检测, 双向交叉尺度融合, 协调注意力, 推理层

Abstract: To provide real-time fire warning based on images，an improved flame and smoke detection algorithm based on YOLOv5s is proposed in this study.The original path aggregation network module is replaced in the Neck of YOLOv5s with a bidirectional cross-scale fusion module，so that the deeper network can directly extract superficial features，enhance information flow，and improve network feature fusion capabilities.The reasoning layer for Coordinate Attention（CA） is added to the Head of YOLOv5s to enhance the ability of extracting and locating network information and improve detection accuracy without increasing the computational burden extensively.A variety of data enhancement technologies，such as Hue，Saturation，Value（HSV） color gamut enhancement，random rotation，and Mosaic，are used to adjust and expand the training data.The k-means clustering algorithm is used to obtain the prior anchor of the dataset to enhance the model robustness.The experimental results demonstrate that compared with the flame and smoke detection algorithm based on YOLOv5s，the mean Average Precision（mAP） of the improved algorithm has increased by 3.2 percentage points，while the detection speed has reached 243 frame/s.The lightweight advantage of YOLOv5s has also been maintained.This is significant for flame and smoke detection in sheltered，dark，small，and multiple targets.

Key words: YOLOv5 network, flame and smoke detection, bidirectional cross-scale fusion, Coordinate Attention（CA）, reasoning layer

中图分类号:

TP399

宋华伟, 屈晓娟, 杨欣, 万方杰. 基于改进YOLOv5的火焰烟雾检测[J]. 计算机工程, 2023, 49(6): 250-256.

SONG Huawei, QU Xiaojuan, YANG Xin, WAN Fangjie. Flame and Smoke Detection Based on Improved YOLOv5[J]. Computer Engineering, 2023, 49(6): 250-256.

http://www.ecice06.com/CN/Y2023/V49/I6/250

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参考文献

[1] LI P,ZHAO W D.Image fire detection algorithms based on convolutional neural networks[J].Case Studies in Thermal Engineering,2020,19:100625.
[2] FOGGIA P,SAGGESE A,VENTO M.Real-time fire detection for video-surveillance applications using a combination of experts based on color,shape,and motion[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(9):1545-1556.
[3] EMMY P C,VINSLEY S S,SURESH S.Multi feature analysis of smoke in YUV color space for early forest fire detection[J].Fire Technology,2016,52(5):1319-1342.
[4] FRIZZI S,KAABI R,BOUCHOUICHA M,et al.Convolutional neural network for video fire and smoke detection[C]//Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society.Washington D.C.,USA:IEEE Press,2016:877-882.
[5] PAWAR A.A multi-disciplinary vision-based fire and smoke detection system[C]//Proceedings of the 4th International Conference on Electronics,Communication and Aerospace Technology.Washington D.C.,USA:IEEE Press,2020:900-904.
[6] VALIKHUJAEV Y,ABDUSALOMOV A,CHO Y I.Automatic fire and smoke detection method for surveillance systems based on dilated CNNs[J].Atmosphere,2020,11(11):1241.
[7] ZHAO Y J,ZHANG H B,ZHANG X L,et al.Fire smoke detection based on target-awareness and depthwise convolutions[J].Multimedia Tools and Applications,2021,80(18):27407-27421.
[8] KHAN S,MUHAMMAD K,HUSSAIN T,et al.DeepSmoke:deep learning model for smoke detection and segmentation in outdoor environments[J].Expert Systems with Applications,2021,182:115125.
[9] MUHAMMAD K,AHMAD J,LÜ Z H,et al.Efficient deep CNN-based fire detection and localization in video surveillance applications[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019,49(7):1419-1434.
[10] 刘丽娟,陈松楠.一种基于改进SSD的烟雾实时检测模型[J].信阳师范学院学报(自然科学版),2020,33(2):305-311.LIU L J,CHEN S N.Real-time smoke detection model based on improved SSD[J].Journal of Xinyang Normal University(Natural Science Edition),2020,33(2):305-311.(in Chinese)
[11] SAPONARA S,ELHANASHI A,GAGLIARDI A.Real-time video fire/smoke detection based on CNN in antifire surveillance systems[J].Journal of Real-Time Image Processing,2021,18(3):889-900.
[12] 朱傥,杨忠,周国兴,等.一种轻量化网络的火焰烟雾检测算法[J].应用科技,2022,49(2):1-7.ZHU T,YANG Z,ZHOU G X,et al.A flame and smoke detection algorithm of the lightweight network[J].Applied Science and Technology,2022,49(2):1-7.(in Chinese)
[13] 回天,哈力旦·阿布都热依木,杜晗.结合Faster R-CNN的多类型火焰检测[J].中国图象图形学报,2019,24(1):73-83.HUI T,Halidan Abudureyimu,DU H.Multi-type flame detection combined with faster R-CNN[J].Journal of Image and Graphics,2019,24(1):73-83.(in Chinese)
[14] 陈金鹏,孙浩,东辉,等.采用卷积神经网络的烟火智能识别算法[J].福州大学学报(自然科学版),2021,49(3):309-315.CHEN J P,SUN H,DONG H,et al.A convolutional neural network adopted algorithm for intelligent fire recognition[J].Journal of Fuzhou University(Natural Science Edition),2021,49(3):309-315.(in Chinese)
[15] 谢书翰,张文柱,程鹏,等.嵌入通道注意力的YOLOv4火灾烟雾检测模型[J].液晶与显示,2021,36(10):1445-1453.XIE S H,ZHANG W Z,CHENG P,et al.Firesmoke detection model based on YOLOv4 with channel attention[J].Chinese Journal of Liquid Crystals and Displays,2021,36(10):1445-1453.(in Chinese)
[16] HU Y W,ZHAN J L,ZHOU G X,et al.Fast forest fire smoke detection using MVMNet[J].Knowledge-Based Systems,2022,241:108219.
[17] ZHAO L,ZHI L Q,ZHAO C,et al.Fire-YOLO:a small target object detection method for fire inspection[J].Sustainability,2022,14(9):4930.
[18] BOCHKOVSKIY A,WANG C Y,LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[EB/OL].[2022-03-19].https://arxiv.org/abs/2004.10934.
[19] WANG C Y,MARK LIAO H Y,WU Y H,et al.CSPNet:a new backbone that can enhance learning capability of CNN[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.Washington D.C.,USA:IEEE Press,2020:1571-1580.
[20] HE K M,ZHANG X Y,REN S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[21] LIU S,QI L,QIN H F,et al.Path aggregation network for instance segmentation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:8759-8768.
[22] LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:936-944.
[23] AKSOY T,HALICI U.Analysis of visual reasoning on one-stage object detection[EB/OL].[2022-03-19].https://arxiv.org/abs/2202.13115.
[24] HOU Q B,ZHOU D Q,FENG J S.Coordinate attention for efficient mobile network design[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2021:13708-13717.
[25] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:7132-7141.
[26] WOO S,PARK J,LEE J Y,et al.CBAM:convolutional block attention module[EB/OL].[2022-03-19].https://arxiv.org/pdf/1807.06521.pdf.
[27] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:common objects in context[M].Berlin,Germany:Springer,2014.
[28] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[29] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2022-03-19].https://arxiv.org/abs/2202.13115.
[30] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[M].Berlin,Germany:Springer,2016.
[31] SANDLER M,HOWARD A,ZHU M L,et al.MobileNetV2:inverted residuals and linear bottlenecks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:4510-4520.
[32] REDMON J,FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2022-03-19].https://arxiv.org/abs/1804.02767.
[33] LI X,PAN J,XIE F,et al.Fast and accurate green pepper detection in complex backgrounds via an improved YOLOV4-tiny model[J].Computers and Electronics in Agriculture,2021,191:106503.

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