Residential smoke detection and identification system based on Yolov3

Pu Liu; Zhili Zhang; Xinghui Zhang

doi:10.1117/12.2643491

9 October 2022 Residential smoke detection and identification system based on Yolov3

Pu Liu, Zhili Zhang, Xinghui Zhang

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122460B (2022) https://doi.org/10.1117/12.2643491
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

Fire seriously endangers people's safety. As an important feature of early fire, smoke should be the primary target of fire early warning detection. Aiming at the shortcomings of traditional fire detection accuracy and real-time performance, a smoke detection model based on Yolov3 is proposed. Since there is no community smoke dataset on the Internet, I established my own dataset in various ways, and then improved the Yolov3 network through multi-scale detection training and K-means-dimensional clustering, so that the training network has a certain pertinence in community smoke detection. The features extracted by the Darknet-53 network are reassembled, and multi-scale training is performed using GPU to obtain the optimal weight model to detect smoke targets in images. The experimental results show that under the same data set, the experimental model can detect 96%, and the test set of the community smoke data set also reaches 91.34%. The speed can also be detected in real time, and the smoke and alarm can be detected in advance, which has practical value.

Citation Download Citation

Pu Liu, Zhili Zhang, and Xinghui Zhang "Residential smoke detection and identification system based on Yolov3", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122460B (9 October 2022); https://doi.org/10.1117/12.2643491

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KEYWORDS

Detection and tracking algorithms

Data modeling

Target detection

Cameras

Flame detectors

Image processing

Imaging systems

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