Research and Implementation of Forest Fire Detection Algorithm Improvement

[1] Liao Shujiang. A preliminary study on the trend of fire spread [J]. Fire Science and Technology, 2012, 31(7): 670–673. Shujiang Liao A preliminary study on the trend of fire spread [J] . Fire Science and Technology , 2012 , 31 ( 7 ): 670 – 673 . Search in Google Scholar

[2] Wang M. Risk Information, Risk Perception and Fire Prevention Behavior [D]. University of Science and Technology of China, 2017. Wang M. Risk Information, Risk Perception and Fire Prevention Behavior [D] . University of Science and Technology of China , 2017 . Search in Google Scholar

[3] Lv P T, Li J, Wu L Y et al. Research on automatic edge detection of fire video images [J]. Applied Science. 2003. Lv P T Li J Wu L Y Research on automatic edge detection of fire video images [J] . Applied Science . 2003 . Search in Google Scholar

[4] Yan Yunyang, Gao Shangbing, Guo Zhibo, et al. Automatic fire detection based on video images [J]. Computer Application Research, 2008, 25(4): 1075–1078. Yunyang Yan Shangbing Gao Zhibo Guo Automatic fire detection based on video images [J] . Computer Application Research , 2008 , 25 ( 4 ): 1075 – 1078 . Search in Google Scholar

[5] Tan Yong, Xie Linbai, Feng Hongwei, et al. Imagebased flame detection algorithm [J]. Laser & Optoelectronics Progress, 2019, 56(16): 161012. Yong Tan Linbai Xie Hongwei Feng Imagebased flame detection algorithm [J] . Laser & Optoelectronics Progress , 2019 , 56 ( 16 ): 161012 . Search in Google Scholar

[6] Cui Bingcheng, Cheng Naiwei, Zhao Peng. Exploration of smoke image detection method based on matlab [J]. Science and Technology Innovation. 2019, (28). Bingcheng Cui Naiwei Cheng Peng Zhao Exploration of smoke image detection method based on matlab [J] . Science and Technology Innovation . 2019 ,(28 ). Search in Google Scholar

[7] Gong F, Li C, Gong W, et al. A real-time fire detection method from video with multifeature fusion [J]. Computational intelligence and neuroscience, 2019, 2019. Gong F Li C Gong W A real-time fire detection method from video with multifeature fusion [J] . Computational intelligence and neuroscience , 2019 , 2019 . Search in Google Scholar

[8] Li P, Zhao W. Image fire detection algorithms based on convolutional neural networks [J]. Case Studies in Thermal Engineering, 2020, 19: 100625. Li P Zhao W. Image fire detection algorithms based on convolutional neural networks [J] . Case Studies in Thermal Engineering , 2020 , 19 : 100625 . Search in Google Scholar

[9] Saeed F, Paul A, Karthigaikumar P, et al. Convolutional neural network based early fire detection [J]. Multimedia Tools and Applications, 2020, 79: 9083–9099. Saeed F Paul A Karthigaikumar P Convolutional neural network based early fire detection [J] . Multimedia Tools and Applications , 2020 , 79 : 9083 – 9099 . Search in Google Scholar

[10] Zhang Chi, Meng Qinghao, Well Tao. Video flame detection algorithm based on improved GMM and multi-feature fusion [J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410006. Chi Zhang Qinghao Meng Tao Well Video flame detection algorithm based on improved GMM and multi-feature fusion [J] . Laser & Optoelectronics Progress , 2021 , 58 ( 4 ): 0410006 . Search in Google Scholar

[11] Jing K, Jia Y, Zhang C, et al. MobileAttentionNet: An Efficient Network for Semantic Segmentation of Forest Fire Images [C]//2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT). IEEE, 2021: 377–380. Jing K Jia Y Zhang C MobileAttentionNet: An Efficient Network for Semantic Segmentation of Forest Fire Images [C] // 2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT) . IEEE , 2021 : 377 – 380 . Search in Google Scholar

[12] Zhang L, Wang M, Ding Y, et al. MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection [J]. Forests, 2023, 14(3): 616. Zhang L Wang M Ding Y MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection [J] . Forests , 2023 , 14 ( 3 ): 616 . Search in Google Scholar

[13] Zhang X, Qian K, Jing K, et al. Fire detection based on convolutional neural networks with channel attention [C]//2020 Chinese Automation Congress (CAC). IEEE, 2020: 3080–3085. Zhang X Qian K Jing K Fire detection based on convolutional neural networks with channel attention [C] // 2020 Chinese Automation Congress (CAC) . IEEE , 2020 : 3080 – 3085 . Search in Google Scholar

[14] Zhao Yuanyuan, Zhu Jun, Xie Yakun, et al. Improved Yolo-v3 algorithm for real-time flame detection in video images [J]. Journal of Wuhan University (Information Science Edition), 2021, 46(3): 326–334. Yuanyuan Zhao Jun Zhu Yakun Xie Improved Yolo-v3 algorithm for real-time flame detection in video images [J] . Journal of Wuhan University (Information Science Edition) , 2021 , 46 ( 3 ): 326 – 334 . Search in Google Scholar

[15] Ding Hao, Wang Huiqin, Wang Ke. Improved YOLOv3 flame detection algorithm based on dynamic shape feature extraction and enhancement [J]. Laser & Optoelectronics Progress, 2022, 59(24):2410003-2410003-9. Hao Ding Huiqin Wang Ke Wang Improved YOLOv3 flame detection algorithm based on dynamic shape feature extraction and enhancement [J] . Laser & Optoelectronics Progress , 2022 , 59 ( 24 ): 2410003-2410003-9 . Search in Google Scholar

[16] Avazov K, Mukhiddinov M, Makhmudov F, et al. Fire detection method in smart city environments using a deep-learning-based approach [J]. Electronics, 2021, 11(1): 73. Avazov K Mukhiddinov M Makhmudov F Fire detection method in smart city environments using a deep-learning-based approach [J] . Electronics , 2021 , 11 ( 1 ): 73 . Search in Google Scholar

[17] Sun J, Ge H, Zhang Z. AS-YOLO: An improved YOLOv4 based on attention mechanism and SqueezeNet for person detection [C]//2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021, 5: 1451–1456. Sun J Ge H Zhang Z. AS-YOLO: An improved YOLOv4 based on attention mechanism and SqueezeNet for person detection [C] // 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) . IEEE , 2021 , 5 : 1451 – 1456 . Search in Google Scholar

[18] Xue Q, Lin H, Wang F. Fcdm: an improved forest fire classification and detection model based on yolov5 [J]. Forests, 2022, 13(12): 2129. Xue Q Lin H Wang F. Fcdm: an improved forest fire classification and detection model based on yolov5 [J] . Forests , 2022 , 13 ( 12 ): 2129 . Search in Google Scholar

[19] Xue Z, Lin H, Wang F. A small target forest fire detection model based on YOLOv5 improvement[J]. Forests, 2022, 13(8): 1332. Xue Z Lin H Wang F. A small target forest fire detection model based on YOLOv5 improvement[J] . Forests , 2022 , 13 ( 8 ): 1332 . Search in Google Scholar

[20] Yang T, Xu S, Li W, et al. A smoke and flame detection method using an improved yolov5 algorithm [C]//2022 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2022: 366371. Yang T Xu S Li W A smoke and flame detection method using an improved yolov5 algorithm [C] // 2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) . IEEE , 2022 : 366371 . Search in Google Scholar

[21] Yang X, Wang Z, He Y, et al. Research on open flame recognition algorithm in construction site based on attention mechanism [C]//2023 15th International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2023: 1–6. Yang X Wang Z He Y Research on open flame recognition algorithm in construction site based on attention mechanism [C] // 2023 15th International Conference on Advanced Computational Intelligence (ICACI) . IEEE , 2023 : 1 – 6 . Search in Google Scholar

[22] Zhang H, Wang Y, Dayoub F, et al. VariFocalnet: An iou-aware dense object detector [C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 8514–8523. Zhang H Wang Y Dayoub F VariFocalnet: An iou-aware dense object detector [C] // Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2021 : 8514 – 8523 . Search in Google Scholar

[23] Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module [C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3–19. Woo S Park J Lee J Y Cbam: Convolutional block attention module [C] // Proceedings of the European conference on computer vision (ECCV) . 2018 : 3 – 19 . Search in Google Scholar