Flame detection method based on improved YOLO-v3

Ping Huo; Fang Lv; Si Chen

doi:10.1117/12.2600353

1 June 2021 Flame detection method based on improved YOLO-v3

Ping Huo, Fang Lv, Si Chen

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 118481D (2021) https://doi.org/10.1117/12.2600353
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

This paper proposes a flame detection method based on the deep learning target detection algorithm YOLOv3 (You Only Look Once). The adaptive channel enhancement module (SE Module) proposed in SENet (Squeeze-and-Excitation Networks) is integrated into YOLOv3, so that the network can focus on learning more important feature information, and increase the detection accuracy and reliability of the network. Aiming at the characteristics of flames, this paper uses the characteristics of YOLOv3 multi-scale detection and adds a fourth detection scale to improve the network's detection of small flame areas. Experiments show that the improved YOLOv3 algorithm can effectively detect flames of different shapes in various backgrounds, and improve the accuracy and recall rate of the model without affecting the detection rate.

Citation Download Citation

Ping Huo, Fang Lv, and Si Chen "Flame detection method based on improved YOLO-v3", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 118481D (1 June 2021); https://doi.org/10.1117/12.2600353

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