COMPUTER VISION BASED EARLY FIRE-DETECTION AND FIREFIGHTING MOBILE ROBOTS ORIENTED FOR ONSITE CONSTRUCTION

被引:0
|
作者
Kong, Liulin [1 ]
Li, Jichao [2 ]
Guo, Shengyu [2 ]
Zhou, Xiaojie [2 ]
Wu, Di [3 ]
机构
[1] China Univ Geosci Wuhan, Fac Engn, Wuhan, Peoples R China
[2] China Univ Geosci Wuhan, Sch Econ & Management, Wuhan, Peoples R China
[3] North China Univ Water Resources & Elect Power, Sch Water Conservancy, Zhengzhou, Henan, Peoples R China
基金
中国国家自然科学基金;
关键词
convolutional neural network; firefighting; fire accidents prevention; mobile robot; improved YOLOv3-Tiny model; construction sites; SURVEILLANCE; NETWORKS; DESIGN; JET;
D O I
10.3846/jcem.2024.21360
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
per proposes a video-based firefighting mobile robot (FFMR), which is designed to patrol the desired territory and will constantly observe for fire-related events to make sure the camera without any occlusions. Once a fire is detected, the early warning system will send sound and light signals instantly and the FFMR moves to the right place to fight the fire source using the extinguisher. To improve the accuracy and speed of fire detection, an improved YOLOv3-Tiny (namely as YOLOv3-Tiny-S) model is proposed by optimizing its network structure, introducing a Spatial Pyramid Pooling (SPP) module, and refining the multi-scale anchor mechanism. The experiments show the proposed YOLOv3-Tiny-S model based FFMR can detect a small fire target with relatively higher accuracy and faster speed under the occlusions by outdoor environment. The proposed FFMR can be helpful to disaster management systems, avoiding huge ecological and economic losses, as well as saving a lot of human lives.
引用
收藏
页码:720 / 737
页数:18
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