Design and application of a fire detection and early warning robot in construction site

doi:10.16265/j.cnki.issn1003-3033.2021.04.019

Abstract

Abstract: In order to prevent fire accidents at construction sites and improve fire detection performance, a detection and early warning robot based on combination of SSD and MobileNet model was designed. Firstly, data set of collected fire video images was processed and divided into two categories, namely real fire images and non-fire images. Secondly, network parameter values in SSD_MobileNet model were fine-tuned based on idea of migration learning. Finally, the obtained model was integrated into robot video surveillance platform for fire detection and early warning, and its application effect was tested on a construction site. The results show that the robot can accurately detect fire in construction sites with a 50% confidence threshold of detection model. Accuracy of multi-frame video exceeds 90%, and real-time warning can be achieved.

Key words: construction site, fire detection, fire warning, robot, single shot multibox detector (SSD)_MobileNet

CLC Number:

X924.3

LI Jichao, GUO Shengyu, KONG Liulin, YUAN Yican, SUN Yang. Design and application of a fire detection and early warning robot in construction site[J]. China Safety Science Journal, 2021, 31(4): 141-146.

References

[1] 李云, 张晴, 沈子豪, 等. FMF的火焰显著性检测[J]. 中国安全科学学报, 2019, 29(5): 56-61.
LI Yun, ZHANG Qing, SHEN Zihao, et al. Flame saliency detection based on FMF [J]. China Safety Science Journal, 2019, 29(5): 56-61.
[2] U.S. Fire Administration. U.S. fire statistics [EB/OL]. [2020-08-01]. https://www.usfa.fema.gov/data/statistics/.
[3] 应急管理部消防救援局. 2019年全国接报火灾23.3万件[EB/OL]. [2020-08-01].https://www.119.gov.cn/article/3xBeEJjR54K.
[4] 何永勃, 董玉珊, 雷建, 等. 飞机货舱火灾多传感器探测方法研究 [J]. 中国安全科学学报, 2018, 28(5): 74-79.
HE Yongbo, DONG Yushan, LEI Jian, et al. Research on multi-sensor method for detecting aircraft cargo fire [J]. China Safety Science Journal, 2018, 28(5): 74-79.
[5] QIU Xuanbing, XI Tingyu, SUN Dongyuan, et al. Fire detection algorithm combined with image processing and flame emission spectroscopy [J]. Fire Technology, 2018, 54(5): 1 249-1 263.
[6] 张科, 叶影, 张红. 基于边缘计算的森林火警监测系统[J]. 大数据, 2019, 5(2): 79-88.
ZHANG Ke, YE Ying, ZHANG Hong. Forest fire monitoring system based on edge computing [J]. Big Data Research, 2019, 5(2): 79-88.
[7] 杨柳, 张德, 王亚慧, 等. 城市火灾视频监控目标区域图像准确检测仿真[J]. 计算机仿真, 2019, 36(4): 425-431.
YANG Liu, ZHANG De, WANG Yahui, et al. A detection and simulation algorithm for video surveillance in urban fire [J].Computer Simulation, 2019, 36(4): 425-431.
[8] 王琳, 姚新, 雷丹. 公路隧道火灾初期视频火焰检测[J]. 中国公路学报, 2018, 31(11): 121-129.
WANG Lin, YAO Xin, LEI Dan. Video-based on recognition of early fire flame in road tunnel [J].China Journal of Highway and Transport, 2018, 31(11): 121-129.
[9] 段锁林, 顾川林. 基于BP神经网络视频火灾火焰检测方法[J]. 常州大学学报:自然科学版, 2017, 29(2): 65-70.
DUAN Suolin, GU Chuanlin. Rsearch on the detection method based on the optimized BP neural network for the visual fire flame recognition [J]. Journal of Changzhou University:Natural Science Edition, 2017, 29(2): 65-70.
[10] MUHAMMAD K, AHMAD J, BAIK S W. Early fire detection using convolutional neural networks during surveillance for effective disaster management [J]. Neurocomputing, 2018, 288: 30-42.
[11] 严云洋, 朱晓妤, 刘以安, 等. 基于Faster R-CNN模型的火焰检测[J]. 南京师大学报:自然科学版, 2018, 41(3): 1-5.
YAN Yunyang, ZHU Xiaoyu, LIU Yi'an, et al. Flame detection based on faster R-CNN model [J]. Journal of Nanjing Normal University:Natural Science Edition, 2018, 41(3): 1-5.
[12] JIAO Zhentian, ZHANG Youmin, XIN Jing, et al. A deep learning based forest fire detection approach using UAV and YOLOv3 [C]. 2019 1^st International Conference on Industrial Artificial Intelligence (IAI), 2019: 1-5.
[13] 李俊伟. 自主巡逻机器人行进避障及火灾预警研究 [D]. 呼和浩特:内蒙古工业大学, 2017.
LI Junwei. Research on obstacle avoidance and fire early warning of autonomous patrol robot [D]. Hohhot: Inner Mongolia University of Technology, 2017.
[14] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector [C]. European Conference on Computer Vision, 2016: 21-37.
[15] HOWARD A G, ZHU Menglong, CHEN Bo, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications [Z/OL]. (2017-04-17). https://arxiv.org/abs/1704.04861.
[16] 任锴, 陈俊, 叶宇煌, 等. 基于SSD-MobileNet的火情检测预警系统 [J]. 电气开关, 2020, 58(1): 34-38.
REN Kai, CHEN Jun, YE Yuhuang, et al. Fire detection early warning system based on SSD-Mobile net [J]. Electric Switchgear, 2020, 58(1): 34-38.
[17] EVERINGHAM M, WINN J. The pascal visual object classes challenge 2007 (voc2007) development kit [J]. International Journal of Computer Vision, 2006, 111(1): 98-136.
[18] 刘鑫鹏, 栾悉道, 谢毓湘,等. 迁移学习研究和算法综述[J]. 长沙大学学报, 2018, 32(5): 28-31.
LIU Xinpeng, LUAN Xidao, XIE Yuxiang, et al. Transfer learning research and algorithm review [J]. Journal of Changsha University, 2018, 32(5): 28-31.
[19] 任宇杰, 杨剑, 刘方涛, 等. 基于SSD和MobileNet网络的目标检测方法的研究[J]. 计算机科学与探索, 2019, 13(11): 1 881-1 893.
REN Yujie, YANG Jian, LIU Fangtao, et al. Research on target detection method based on SSD and mobilenet network[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(11): 1 881-1 893.