Table 10 Comparative analysis of proposed models with respect to different deep learning models
From: Forest fire and smoke detection using deep learning-based learning without forgetting
References | Models | Accuracy (%) |
|---|---|---|
(Li and Zhao 2020) | YOLO v3 | 83.7 |
(Mahmoud 2022) | Deep ANN and AlexNet | 95 and 98 |
(Cheng 2021) | VGG16 with TL | 97.83 |
(Guede-Fernández et al. 2021) | Faster R-CNN | 80 |
(Luo et al. 2018) | CNN | 90 |
(Muhammad et al. 2018) | CNN | 94.39 |
(Jeon et al. 2021) | CNN with feature-squeeze block | 97.89 |
Proposed models | VGG16 – Feature Extractor | 94.38 |
VGG16 – Fine Tuner | 95.46 | |
InceptionV3 – Feature Extractor | 92.04 | |
InceptionV3 – Fine Tuner | 97.01 | |
Xception – Feature Extractor | 97.77 | |
Xception – Fine Tuner | 98.72 |