|
|
|
| An Improved YOLOv5s Forest Fire Smoke Detection Algorithm |
| ZHANG Liguo,ZHANG Qi,JIN Mei,YUAN Yulin,WANG Hongfeng |
| School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract A forest fire smoke detection algorithm based on improved YOLOv5s is proposed. A fire and smoke dataset containing 16573 images is constructed to solve the problem of insufficient training data sets and improve the generalization ability of the training model. A lightweight GC-C3 module is designed to replace the original C3 module and reduce the number of model parameters and calculation. The weighted bidirectional feature pyramid network is integrated into the Neck structure to enhance the detection ability of the network for small and medium targets. The network space pyramid pool structure is modified, SPPF is replaced by SimSPPF structure, and the computing efficiency and detection accuracy of the network are improved. The bounding box regression loss function CIOU is replaced by Focal-EIOU to accelerate the convergence of the model and solve the problem of mismatch between positive and negative samples. The experimental results show that the average detection accuracy of the improved network is increased by 2.3%, the number of model parameters is decreased by 46.7%, and the calculation amount of the model is decreased by 47.5%.
|
|
Received: 04 February 2024
Published: 26 September 2024
|
|
|
|
|
|
| ［2］ |
HU Y, ZHAN J, ZHOU G, et al.Fast forest fire smoke detection using MVMNet［J］. Knowledge-Based Systems, 2022, 241: 108219.
|
| ［4］ |
LI X, LIU J, HUANG Y, et al.Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion［J］. Micromachines, 2022, 13(8): 1205.
|
| ［6］ |
EMMY PREMA 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.
|
| ［8］ |
PAN J, OU X, XU L.A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN［J］. Forests, 2021, 12(6): 768-768.
|
| ［9］ |
郑远攀, 许博阳, 王振宇.改进的YOLOv5烟雾检测模型［J］. 计算机工程与应用, 2023, 59(7): 214-221.
|
| ［10］ |
ZHOU W, CONRAD A B, RAHIM H S, et al.Image quality assessment: from error visibility to structural similarity［J］. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
|
| ［12］ |
TAN M, PANG R, LE Q V.EfficientDet: Scalable and Efficient Object Detection［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Seattle, USA, 2020: 10778-10787.
|
| ［14］ |
朱文超, 杨洁, 何超.林区视频监控下车流量分类统计［J］. 计量学报, 2023, 44(7): 1093-1099.
|
| ［16］ |
LIU S, QI L, QIN H, et al.Path Aggregation Network for Instance Segmentation［C］//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Salt Lake City, USA, 2018: 8759-8768.
|
| ［17］ |
WANG C Y, LIAO H M, WU Y H, et al.CSPNet: A New Backbone that can Enhance Learning Capability of CNN［C］//IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).Seattle, USA, 2020: 1571-1580.
|
| ［19］ |
ZHENG Z, WANG P, LIU W, et al.Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression［J］. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12993-13000.
|
| ［21］ |
HOU Q, ZHOU D, FENG J.Coordinate Attention for Efficient Mobile Network Design［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Nashville, USA.2021: 13713-13722.
|
| ［1］ |
SCHOLTEN R C, JANDT R, MILLER E A, et al.Overwintering fires in boreal forests［J］. Nature, 2021, 593(7859): 399-404.
|
| ［3］ |
ZHENG R, ZHANG D, LU S, et al.Discrimination Between Fire Smokes and Nuisance Aerosols Using Asymmetry Ratio and Two Wavelengths［J］. Fire Technology, 2019, 55(5): 1753-1770.
|
| ［5］ |
GUBBI J, MARUSIC S, PALANISWAMI M.Smoke detection in video using wavelets and support vector machines［J］. Fire Safety Journal, 2009, 44(8): 1110-1115.
|
| ［7］ |
吴凤和, 崔健新, 张宁, 等.基于改进YOLOv4算法的轮毂表面缺陷检测［J］. 计量学报, 2022, 43(11): 1404-1411.
|
|
WU F H, CUI J X, ZHANG N, et al.Surface Defect Detection of Wheel Hub Based on Improved YOLOv4 Algorithm［J］. Acta Metrologica Sinica, 2022, 43(11): 1404-1411.
|
|
ZHENG Y P, XU B Y, WANG Z Y.Improved YOLOv5 Smoke Detection Model［J］. Computer Engineering And Applications, 2023, 59(7): 214-221.
|
| ［11］ |
TANG Y, HAN K, GUO J, et al.GhostNetv2: Enhance cheap operation with long-range attention［J］. Advances in Neural Information Processing Systems, 2022, 35: 9969-9982.
|
| ［13］ |
ZHANG Y F, REN W, ZHANG Z, et al.Focal and efficient IOU loss for accurate bounding box regression［J］. Neurocomputing, 2022, 506: 146-157.
|
|
ZHU W C, YANG J, HE C.Traffic Classification Statistics Under Video Surveillance in Forest Areas［J］. Acta Metrologica Sinica, 2023, 44(7): 1093-1099.
|
| ［15］ |
LIN T Y, DOLLAR P, GIRSHICK R, et al.Feature Pyramid Networks for Object Detection［C］// IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Hawaii, USA, 2017: 936-944.
|
| ［18］ |
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.
|
| ［20］ |
HAN K, WANG Y, TIAN Q, et al.GhostNet: More Features From Cheap Operations［C］//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Seattle, USA, 2020: 1577-1586.
|
| ［22］ |
张立国, 蒋轶轩, 田广军.基于多尺度融合方法的无人机对地车辆目标检测算法研究［J］. 计量学报, 2021, 42(11): 1436-1442.
|
| ［23］ |
HU J, SHEN L, SUN G.Squeeze-and-Excitation Networks［C］//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Salt Lake City, USA, 2018: 7132-7141.
|
|
ZHANG L G, JIANG Y X, TIAN G J.Research on Unmanned Aerial Vehicle to Ground Vehicle Target Detection Algorithm Based on Multiscale Fusion Method［J］. Acta Metrologica Sinica, 2021, 42(11): 1436-1442.
|
| ［25］ |
WANG Q, WU B, ZHU P, et al.ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks［C］// IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Seattle, USA.2020: 11531-11539.
|
| ［26］ |
GEVORGYAN Z. SIoU loss: more powerful learning for bounding box regression［J］. arXiv e-prints, 2022. DOI:10.48550/arXiv.2205.12740.
|
| ［24］ |
WOO S, PARK J, LEE J Y, et al.CBAM: Convolutional block attention module［C］//Proceedings of the European conference on computer vision (ECCV).Munich, Germany, 2018: 3-19.
|
| ［27］ |
HE J, ERFANI S, MA X, et al.Alpha-IoU: A family of power intersection over union losses for bounding box regression［J］. Advances in Neural Information Processing Systems, 2021, 34: 20230-20242.
|
|
|
|
2024, Vol. 45 
