Table 4.
Detection-based methods for forest fire monitoring and surveillance.
| Authors | Year | Method | Architecture | Accuracy | Application | Augmentation | Type of Data |
|---|---|---|---|---|---|---|---|
| Hung et al. [68] | 2019 | DN-CNN | Faster R-CNN, Hidden Markov Model (HMM) | Detection rate - 96% | Detection | Yes | Image & Video |
| Jiao et al. [67] | 2019 | CNN | YOLOv3 | the detection rate can reach 83%. | Detection | No | UAV Image |
| Li et al. [76] | 2021 | h-EfficientDet | EfficientDet and h-EfficientDet | Accuracy - 98.35% | Detection | No | Image |
| Peng and Wang [88] | 2022 | CNN | SqueezeNet1.1, AlexNet, MobileNetV3 Large and Small MobileNetV1 0.25 & 1.0, MobileNetV2 0.25 & 1.0, ResNet18, & VGG-16 | Accuracy - 99.28%. | Detection | No | Image |
| Li et al. [85] | 2022 | CNN | YOLOv3, YOLO-LITE, Tinier-YOLO | mAP - 96.05% | Detection | No | Image |
| Mohnish et al. [40] | 2022 | CNN | CNN | Accuracy - 92.20% | Detection | Yes | Image |
| Tahir et al. [86] | 2022 | CNN | YOLOv5 | F1-score - 94.44%. | Detection | Yes | UAV Image |
| Wang et al. [84] | 2022 | CNN | YOLO | Accuracy - 83.9% | Detection | No | Image |
| Almasoud [91] | 2023 | IWFFDA-DL, ACNN-BLSTM | ACNN-BLSTM optimized BFO & YOLO v3 | Accuracy - 99.56%. | Detection | No | Image |
| Ciprián-Sánchez et al. [72] | 2021 | CNN | Fire-GAN, VGG-19 | Information entropy EN - 10 | Classification | Yes | Image |
| Xie and Huang [93] | 2023 | Transfer Learning and Improved Faster RCNN | ResNet50 network and Faster RCNN with feature fusion and attention | Accuracy - 93.7% | Detection | No | UAV image |