An improved Yolov3 assembled with GMM for laboratory fire smoke detection

Jun Zhao; Fei Sun; Yuhang Ding; Shenxin Liu; Zifeng Wang; Jie Huang

doi:10.1117/12.2681057

8 June 2023 An improved Yolov3 assembled with GMM for laboratory fire smoke detection

Jun Zhao, Fei Sun, Yuhang Ding, Shenxin Liu, Zifeng Wang, Jie Huang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072G (2023) https://doi.org/10.1117/12.2681057
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

In order to effectively prevent the occurrence of fire accidents, this paper proposes an improved algorithm for automatic identification of laboratory fire smoke based on GMM+YOLOv3. Firstly, the non-smoke area is removed by GMM foreground extraction to reduce the impact of less smoke in the early stage of fire and more irrelevant background on fire smoke recognition. Then, combined with the EfficientNet-B0 network, the feature extraction network of YOLOv3 is optimized to reduce the number of parameters, so that the model can be deployed in embedded devices to complete detection tasks. Secondly, in order to make it easier to detect small objects, the feature pooling fusion module is used in the model to improve detection accuracy. Finally, the model parameters are improved to enhance the training and detection effects of the network. Experiments have proved that compared with the traditional YOLOv3 target detection algorithm, this method reduces the number of parameters of the model, improves the calculation speed, and increases the accuracy rate by 5.9%, which ensures the accuracy of smoke detection in complex environments and effectively avoiding the fire accident.

Citation Download Citation

Jun Zhao, Fei Sun, Yuhang Ding, Shenxin Liu, Zifeng Wang, and Jie Huang "An improved Yolov3 assembled with GMM for laboratory fire smoke detection", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072G (8 June 2023); https://doi.org/10.1117/12.2681057

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KEYWORDS

Feature extraction

Fire

Target detection

Detection and tracking algorithms

Convolution

Video

Feature fusion

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