# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
## 2 Related Work  
Numerous fire detection methods and algorithms have been proposed in the recent literature. In this section, we review some works related to wildfire full-frame classification. [PERSON] et al. in [6], used simplified and experimentally optimized AlexNet and InceptionV1 CNN architectures for a frame-based binary fire detection task. For each architecture, different network configurations were experimentally assessed. For the AlexNet architecture, the authors considered 6 variations by removing a combination of 1-3 different CNN layers, while for InceptionV1, the authors considered 8 variations by removing up to 8 inception modules from the original architecture. The reported results showed that the reduction in model complexity, which also resulted in a reduction in the number of parameters (in millions), improved the classification accuracy. The authors have identified the best-performing model for AlexNet and InceptionV1. The model based on AlexNet was named FireNet, while the model based on InceptionV1 was named InceptionV1-OnFire. The accuracies of the optimized models were compared against the original AlexNet, InceptionV1, and VGG-13 models. The reported results indicated that it is possible to achieve 93% accuracy with InceptionV1-OnFire for the binary classification of fire images with 8.4 fps. The dataset used in this work is a collection of data from [PERSON] et al. [10] (75,683 images), [PERSON] et al. [11] (20,593 images), also known as \"furg-fire-dataset\" [12], and material from public video sources (youtube.com: 269,426 images). The authors extracted 23,408 images from this collection to create a training subset, and 2931 images to create a testing subset. [PERSON] et al. in [7], further explored this approach, i.e., the reduction in the complexity of the CNN model, and they have based their research on new architectures such as Inception, ResNet, and EfficientNet, and their combinations. They explored two detection problems: binary fire detection to detect the presence of fire in a frame and localization to detect the precise location of the fire within the frame. The authors explored InceptionV2, InceptionV3, InceptionV4, ResNet, a hybrid Inception-ResNet which is a combination between Inception and ResNet architectures, and EfficientNet. The authors identified the best variations, which they named as InceptionV3-OnFire and InceptionV4-OnFire. For the full-frame binary fire detection, these methods achieved 94.4% accuracy with InceptionV3-OnFire, and 95.6% accuracy with InceptionV4-OnFire, with 13.8 fps, and 12 fps, respectively. In [8], [PERSON] et al. explored two CNN architectures to improve classification performance for real-time fire detection, which was based on experiments on NasNet-A-Mobile [13] and ShuffleNetV2 architectures [14]. Architectures were chosen because of their compactness and ability to provide high performance on ImageNet classification. The authors used transfer learning technique, whereby some layers within the original network are frozen, some layers are removed, and new layers to be trained on new data for fire classification are added. To be more precise, they removed only the last fully connected layer and added a new linear layer for binary classification. For the first half of training time, the entire architecture was frozen except the final linear layer, whilst for the remaining half of training time, the final convolutional layer in ShuffleNetV2 was unfrozen, and the final two reductions, normal cell iterations for NasNet-A-Mobile, were also unfrozen. The dataset used for training is the same dataset used in [6]. The best models were named NasNet-A-OnFire and ShuffleNetV2-OnFire with respect to the original models. The results indicated comparable performance to the models presented in [7], where NasNet-A-OnFire achieved 95.3% accuracy with 7 fps, and ShuffleNetV2-OnFire achieved 95% accuracy with 40 fps. [PERSON] et al.