Mcan-YOLO: An Improved Forest Fire and Smoke Detection Model Based on YOLOv7

被引:1
|
作者
Liu, Hongying [1 ]
Zhu, Jun [2 ]
Xu, Yiqing [2 ]
Xie, Ling [1 ]
机构
[1] Nanjing Univ Sci & Technol, Zijin Coll, Sch Comp & Artificial Intelligence, Nanjing 210023, Peoples R China
[2] Nanjing Vocat Univ Ind Technol, Sch Comp & Software, Nanjing 210023, Peoples R China
来源
FORESTS | 2024年 / 15卷 / 10期
关键词
forest fire smoke detection; YOLOv7; normalization-based attention module; CARAFE; AFPN;
D O I
10.3390/f15101781
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters.
引用
收藏
页数:22
相关论文
共 50 条
  • [21] Dense Small Object Detection Based on an Improved YOLOv7 Model
    Chen, Xun
    Deng, Linyi
    Hu, Chao
    Xie, Tianyi
    Wang, Chengqi
    APPLIED SCIENCES-BASEL, 2024, 14 (17):
  • [22] Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7
    Huang, Peile
    Wang, Shenghuai
    Chen, Jianyu
    Li, Weijie
    Peng, Xing
    SENSORS, 2023, 23 (16)
  • [23] Improved YOLOv7 model for insulator defect detection
    Wang, Zhenyue
    Yuan, Guowu
    Zhou, Hao
    Ma, Yi
    Ma, Yutang
    Chen, Dong
    ELECTRONIC RESEARCH ARCHIVE, 2024, 32 (04): : 2880 - 2896
  • [24] A Trash Detection Model Based on YOLOv7
    Liang, Hu
    Xu, Chao
    He, Tao
    PROCEEDINGS OF 2024 INTERNATIONAL CONFERENCE ON COMPUTER AND MULTIMEDIA TECHNOLOGY, ICCMT 2024, 2024, : 300 - 303
  • [25] NAM-YOLOV7: An Improved YOLOv7 Based on Attention Model for Animal Death Detection
    Sirisha, Uddagiri
    Chandana, Bolem Sai
    Harikiran, Jonnadula
    TRAITEMENT DU SIGNAL, 2023, 40 (02) : 783 - 789
  • [26] YOLOv7-GCM: a detection algorithm for creek waste based on improved YOLOv7 model
    Qin, Jianhua
    Zhou, Honglan
    Yi, Huaian
    Ma, Luyao
    Nie, Jianhan
    Huang, Tingting
    PATTERN ANALYSIS AND APPLICATIONS, 2024, 27 (04)
  • [27] Underwater Target Detection Based on Improved YOLOv7
    Liu, Kaiyue
    Sun, Qi
    Sun, Daming
    Peng, Lin
    Yang, Mengduo
    Wang, Nizhuan
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 11 (03)
  • [28] Mask wearing detection based on improved YOLOv7
    Fu Hui-chen
    Gao Jun-wei
    Che Lu-yang
    CHINESE JOURNAL OF LIQUID CRYSTALS AND DISPLAYS, 2023, 38 (08) : 1139 - 1147
  • [29] Helmet Detection Algorithm Based on Improved YOLOv7
    Yilihamu, Yaermaimaiti
    Liu, Yajie
    Xi, Lingfei
    Wang, Ruohao
    AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2024, 58 (06) : 642 - 655
  • [30] Ship Detection and Recognition Based on Improved YOLOv7
    Wu, Wei
    Li, Xiulai
    Hu, Zhuhua
    Liu, Xiaozhang
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 76 (01): : 489 - 498