Drone-based Forest fire detection: a comparison of convolutional neural networks with an emphasis on MobileNetV2 enhancements

Shengqi Zhang

doi:10.1117/12.3037815

12 September 2024 Drone-based Forest fire detection: a comparison of convolutional neural networks with an emphasis on MobileNetV2 enhancements

Shengqi Zhang

Author Affiliations +

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325628 (2024) https://doi.org/10.1117/12.3037815
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

Drone image recognition plays a significant role in identifying forest fires. This paper applies convolutional neural networks and explores the effects of category weight setting and attention mechanisms, aiming to find a more accurate model for recognizing forest fires. The paper first compares the performance of DenseNet121, InceptionV3, MobileNetV2, and ResNet50, finding that MobileNetV2 performs exceptionally well. Then, based on MobileNetV2, parameter tuning is carried out, determining the appropriate optimizer Adam, learning rate 0.001, and random seed 11. Subsequently, the paper explores category weight setting and attention mechanisms, ultimately finding that category weight setting has a significant effect, while the role of attention mechanisms is limited under the circumstances of this paper. Begin the abstract two lines below author names and addresses.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengqi Zhang "Drone-based Forest fire detection: a comparison of convolutional neural networks with an emphasis on MobileNetV2 enhancements", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325628 (12 September 2024); https://doi.org/10.1117/12.3037815

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