An Image-Based Fire Monitoring Algorithm Resistant to Fire-like Objects
Author:
Xu Fang1, Zhang Xi1, Deng Tian23ORCID, Xu Wenbo23ORCID
Affiliation:
1. Shenyang Fire Science and Technology Research Institute of MEM, Shenyang 110034, China 2. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China 3. Hubei Key Laboratory of Smart Internet Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract
Due to its wide monitoring range and low cost, visual-based fire detection technology is commonly used for fire detection in open spaces. However, traditional fire detection algorithms have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. These algorithms have poor anti-interference ability against fire-like objects, such as emissions from factory chimneys, clouds, etc. In this study, we developed a fire detection approach based on an improved YOLOv5 algorithm and a fire detection dataset with fire-like objects. We added three Convolutional Block Attention Modules (CBAMs) to the head network of YOLOv5 to improve its feature extraction ability. Meanwhile, we used the C2f module to replace the original C2 module to capture rich gradient flow information. Our experimental results show that the proposed algorithm achieved a mAP@50 of 82.36% for fire detection. In addition, we also conducted a comparison test between datasets with and without labeling information for fire-like objects. Our results show that labeling information significantly reduced the false-positive detection proportion of fire-like objects incorrectly detected as fire objects. Our experimental results show that the CBAM and C2f modules enhanced the network’s feature extraction ability to differentiate fire objects from fire-like objects. Hence, our approach has the potential to improve fire detection accuracy, reduce false alarms, and be more cost-effective than traditional fire detection methods. This method can be applied to camera monitoring systems for automatic fire detection with resistance to fire-like objects.
Funder
National Key Research and Development Program of China Fundamental Research Funds for the Central Universities of HUST
Subject
Earth and Planetary Sciences (miscellaneous),Safety Research,Environmental Science (miscellaneous),Safety, Risk, Reliability and Quality,Building and Construction,Forestry
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