# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
## 5 Discussion  
In this section, we provide a comparison between the six state-of-the-art models discussed in Section 2 and the best models proposed in this work. We measured the performance of all models on the same hardware configuration and provide the results of eight metrics described in Section 4.1. We evaluated the following models:  
1. FireNet [6];
2. InceptionNetV1-OnFire [6];
3. InceptionNetV3-OnFire [7];
4. InceptionNetV4-OnFire [7];
5. NasNet-A-OnFire [8];
6. ShuffleNetV2-OnFire [8];
7. Our LW-FIRE100\({}_{12}\) model, and;
8. Our LW-FIRE150\({}_{42}\) model. Models 1-6 were trained on the dataset presented in [6]. The dataset was created by combining several other datasets, i.e., data from [PERSON] et al. [10] (75,683 images), [PERSON] et al. [11] (20,593 images), also known as the \"furg-fire-dataset\" [12], and material from public video sources (youtube.com: 269,426 images). The authors then extracted 23,408 images from this collection to create a new dataset for training (12,550 images of fire, and 10,858 images of not-fire). The testing subset consists of 2931 images. Models 7-8 were trained on the transformed CFDB dataset, as described in Section 3. The generalization of two of our models was demonstrated not only on the testing subset of this dataset but also on a testing subset from [6]. Besides the model's architecture, better generalization is achieved through the introduction of the dataset transformation technique, which increased the data usage from the dataset. To provide a fair comparison between the presented models, we performed two experiments:  
1. Experiment I: All models have been tested on the Dunnings dataset used in [6]. Results are presented in Table 6;
2. Experiment II: All models have been testes on the dataset configurations created in this work based on the CFDB dataset. Results are presented in Tables 7 and 8, for \(N=12\) and \(N=42\), respectively. In Experiment I, we used a testing subset from [6], which consists of 2931 images extracted from six video sources. Some examples of images are provided in Figure 8, where we show the first and last frame of six video sources from the testing subset. In Table 6, we present the results. Models 1-6 have been evaluated on the original images, while our model LW-FIRE150\({}_{42}\) was evaluated on \(2\times 2\) sub-images. In this case, an image was considered to belong to the \"fire\" class if any of the sub-images has been classified as a \"fire\" image. In Figure 8, we show the performance on \(2\times 2\) sub-images of two models: InceptionOne V4-OnFire, which has the best overall performance on this dataset, and our LW-FIRE150\({}_{42}\). The state-of-the-art models performed better on the original size images,while LW-FIRE15042 performed better on sub-images, as was expected. We evaluated our model on \(2\times 2\) sub-images to show that our model can achieve a higher true positive rate (TPR) than the state-of-the-art models, while in other metrics, it shows comparable results. The results of our model could be improved if the false positive rate (FPR) is improved, which would have a positive effect on other metrics. Our model also outperformed FireNet, InceptionNetV1-OnFire, InceptionNetV3-OnFire, and ShuffleNetV2-OnFire when we consider metrics such as ACC, F1, MCC, and FM. The classification accuracy is demonstrated in Figure 8. For example, InceptionOneV4-OnFire (A) could not classify one of the sub-images as fire in the first frame of Video 1 and 2, while the last frame of Video 2 misclassified a fire truck as a \"fire\". In the first frame of Video 4, the model misclassified smoke as fire on one of the sub-images. In the last frame of Video 4, it failed to classify two sub-images as fire, and in Video 6, one sub-image as