Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data
Dai Quoc Tran, Minsoo Park, Yuntae Jeon, JinYeong Bak, Seunghee Park
Abstract
An effective forest-fire response is critical for minimizing the losses caused by forest fires. The purpose of this study is to construct a model for early fire detection and damage area estimation for response systems based on deep learning. First, a large-scale fire dataset with approximately 400,000 images is used to train and test object-detection models. The optimal backbone for the faster region-based convolutional neural network (Faster R-CNN) model is determined using a DetNAS-based architecture search algorithm. Then, the searched light-weight backbone is compared with well-known backbones, such as ResNet, VoVNet, and FBNetV3. In addition, data pertaining to six years of historical forest fire events are employed to estimate the damaged area. Subsequently, a weather API is used to match the recorded events. A Bayesian neural network (BNN) model is used as a regression model to estimate the damaged area. Additionally, the trained model is compared with other widely used regression models, such as decision trees and neural networks. The Faster R-CNN with a searched backbone achieves a mean average precision of 27.9 on 40,000 testing images, outperforming existing backbones. Compared with other regression models, the BNN estimates the damage area with less error and increased generalization. Thus, both proposed models demonstrate their robustness and suitability for implementation in real-world systems.