Smoke segmentation is a critical task in computer vision, with applications in disaster response, environmental monitoring, and safety assessment, etc. This study presents an efficient method for segmenting smoke using KNN-based background modeling for foreground extraction and the SegFormer semantic segmentation algorithm. The KNN background modeling effectively isolates the explosion smoke regions, generating a large dataset with minimal manual annotation workload. We compare the performance of several semantic segmentation algorithms, namely UNet, DeepLab v3 plus, and SegFormer, using the generated dataset. Our results demonstrate that SegFormer outperforms the other algorithms in terms of accuracy and efficiency. The proposed method not only simplifies the dataset generation process but also provides accurate segmentation of smoke, making it a valuable tool for real-world applications in disaster response and environmental monitoring.


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    Titel :

    Effective Smoke Segmentation Using KNN Background Modeling and SegFormer


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Long, Shengzhao (Herausgeber:in) / Dhillon, Balbir S. (Herausgeber:in) / Yang, Junjie (Autor:in) / Liu, Haoting (Autor:in) / Lv, Meng (Autor:in) / Wang, Mengmeng (Autor:in) / Li, Qing (Autor:in)

    Kongress:

    International Conference on Man-Machine-Environment System Engineering ; 2023 ; Beijing, China October 20, 2023 - October 23, 2023



    Erscheinungsdatum :

    05.09.2023


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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