Forest fire image detection method based on improved CenterNet

Baomin Li; Xiaopeng Wang; Qianrong Sun; Shuailong Yu

doi:10.1117/12.2683346

26 June 2023 Forest fire image detection method based on improved CenterNet

Baomin Li, Xiaopeng Wang, Qianrong Sun, Shuailong Yu

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127211J (2023) https://doi.org/10.1117/12.2683346
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

In order to quickly and accurately identify forest fires, a forest fire detection method based on CenterNet without anchor frame was proposed. Firstly, lightweight backbone CSP-VoVNet is used to improve the backbone network and improve the detection speed. Secondly, the multi-scale fusion was carried out by introducing the weighted bidirectional characteristic pyramid BiFPN of ECA to improve the detection accuracy of small fireworks. Finally, Smooth L1 was used to regression center point offset to improve model robustness. The experimental results show that the method can accurately identify the small size forest fireworks and realize the early warning effect under the premise of high detection speed.

Citation Download Citation

Baomin Li, Xiaopeng Wang, Qianrong Sun, and Shuailong Yu "Forest fire image detection method based on improved CenterNet", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127211J (26 June 2023); https://doi.org/10.1117/12.2683346

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