RepVGG-YOLOv7: A Modified YOLOv7 for Fire Smoke Detection
Author:
Chen Xin1ORCID, Xue Yipeng1ORCID, Hou Qingshan1, Fu Yan2, Zhu Yaolin1
Affiliation:
1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China 2. Shaanxi Architectural Design Research Institute Co., Ltd., Xi’an 710018, China
Abstract
To further improve the detection of smoke and small target smoke in complex backgrounds, a novel smoke detection model called RepVGG-YOLOv7 is proposed in this paper. Firstly, the ECA attention mechanism and SIoU loss function are applied to the YOLOv7 network. The network effectively extracts the feature information of small targets and targets in complex backgrounds. Also, it makes the convergence of the loss function more stable and improves the regression accuracy. Secondly, RepVGG is added to the YOLOv7 backbone network to enhance the ability of the model to extract features in the training phase, while achieving lossless compression of the model in the inference phase. Finally, an improved non-maximal suppression algorithm is used to improve the detection in the case of dense smoke. Numerical experiments show that the detection accuracy of the proposed algorithm can reach about 95.1%, which contributes to smoke detection in complex backgrounds and small target smoke.
Funder
National Natural Science Foundation of China Chinese Postdoctoral Science Foundation Specialized Research Fund for Xi’an University Talent Service Enterprise Project Natural Science Foundation of Shaanxi Province of China Key Research and Development Program of Shaanxi Provincial Science and Technology
Subject
Earth and Planetary Sciences (miscellaneous),Safety Research,Environmental Science (miscellaneous),Safety, Risk, Reliability and Quality,Building and Construction,Forestry
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