SECA-Net: A Lightweight Spatial and Efficient Channel Attention for Enhanced Natural Disaster Recognition
Hamdi, Skander; Moussaoui, Abdelouahab; Chabane, Mafaza; Laouarem, Ayoub; Berrimi, Mohamed; Oussalah, Mourad (2024-12-18)
IEEE
18.12.2024
S. Hamdi, A. Moussaoui, M. Chabane, A. Laouarem, M. Berrimi and M. Oussalah, "SECA-Net: A Lightweight Spatial and Efficient Channel Attention for Enhanced Natural Disaster Recognition," 2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Setif, Algeria, 2024, pp. 1-7, doi: 10.1109/ICT-DM62768.2024.10798955
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202501291384
https://urn.fi/URN:NBN:fi:oulu-202501291384
Tiivistelmä
Abstract:
Disaster management and detection from satellite imagery and different sources are crucial yet challenging tasks. In this work, we introduce SECA-Net, a compact deep learning architecture designed to enhance disaster image classification. Employing a cascade attention mechanism, SECA-Net refines feature maps to improve accuracy significantly, achieving a validation accuracy of 93.45% on a diverse dataset covering four disaster categories, surpassing traditional models. Despite its lightweight size of 24 MB, SECA-Net is robust and generalizable across various environments, making it ideal for use in resource-constrained settings typical of disaster-affected areas. This model offers a promising solution for effective disaster response, combining efficiency, accuracy, and practical applicability in challenging real-world scenarios.
Disaster management and detection from satellite imagery and different sources are crucial yet challenging tasks. In this work, we introduce SECA-Net, a compact deep learning architecture designed to enhance disaster image classification. Employing a cascade attention mechanism, SECA-Net refines feature maps to improve accuracy significantly, achieving a validation accuracy of 93.45% on a diverse dataset covering four disaster categories, surpassing traditional models. Despite its lightweight size of 24 MB, SECA-Net is robust and generalizable across various environments, making it ideal for use in resource-constrained settings typical of disaster-affected areas. This model offers a promising solution for effective disaster response, combining efficiency, accuracy, and practical applicability in challenging real-world scenarios.
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