Smoke Detection Algorithm Based on Improved EfficientDet
Pages 61 - 67
Abstract
In the early stage of fire, smoke alarm detection is an important means to prevent fire. And with the continuous construction of monitoring facilities, it is of great significance for the study of smoke video monitoring. In order to meet the detection accuracy and speed of the video, the EfficientDet target detection algorithm was improved. Firstly, the visual analysis of the smoke data set was carried out by clustering method, and the anchor frame parameters in the EfficientDet algorithm were re-calibrated by K-means clustering method. Secondly, the Bi-FPN feature fusion algorithm is improved to reduce the transverse cross-layer connection and increase the longitudinal cross-layer connection, which reduces the calculation of parameters and improves the detection accuracy. Finally, in order to solve the problem of missing detection in small smoke area, a two-channel attention mechanism is added to improve the detection accuracy.
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Published In
July 2022
155 pages
ISBN:9781450396936
DOI:10.1145/3556677
Copyright © 2022 ACM.
© 2022 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 October 2022
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- The National Natural Science Foundation of China
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ICDLT 2022
ICDLT 2022: 2022 6th International Conference on Deep Learning Technologies
July 26 - 28, 2022
Xi'an, China
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