# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 5 Discussion  
Although interest in using DL-based computer vision techniques to detect fire and smoke in forests and natural protected areas has increased, the methods used remain limited to classical deep learning models. [PERSON] et al. [41] conducted a study on smoke detection using a Wildfire Smoke Dataset. In their study, the authors aimed to improve the performance of the object detection model by performing data augmentation. After the augmentation process, 1915 image data were obtained, and all the image data obtained were run on the Yolov5-x, Faster R-CNN, SSD, Yolov3, and Fast R-CNN models; mAP values of 87%, 79%, 72%, 81%, and 78% were obtained, respectively. As can be seen, authors stated that Yolov5-x gave the highest result with a mAP value of 87% among the five different DL models. In addition, the mAP values of the Yolov5-x model, when operated with different batch size values, are also given. The highest mAP value (87%) was achieved with a batch size of 16. Some innovations have been introduced to improve the performance and increase the accuracy of detection. [PERSON] et al. [42] used the Wildfire Smoke Dataset for smoke detection. By performing data augmentation, total data was increased to 1723. In the dataset, 80% of the image data were reserved for training and 20% for testing. The results were compared by running five models (Yolov5, Yolov7, SSD, Yolov4, Fast R-CNN, EfficientDet, Yolov3, and Faster R-CNN) with the image data obtained. While five models, Yolov5, Yolov7, and Yolov3, reached over 90% inmAP value, the other models remained below 80% in mAP value. Yolov3's [43] accuracy is as good as that of SSD [24] but three times faster than SSD. An SPP block is added that combines multiple max-pooling outputs. By combining feature maps at different scales with multi-scale prediction, the problem of not being able to detect small objects was solved. Yolov3 has an architecture that connects the inputs of 1 \(\times\) 1 layers to the outputs of 3 \(\times\) 3 layers, consisting of 106 convolutional layers, 53 of which are in the detection head, and includes Leaky ReLU activation and BN structures. It works slightly slower because of the deep layers it contains. On the other hand, in this study the results of all evaluations indicated that the model with the highest performance was Yolov5-x, with a mAP value of 96.8%. Distribution-based methods train DNNs as feature extractors to detect abnormal images and treat images deviating from the center of the distribution as abnormal inputs. However, they cannot find the underlying image part that causes abnormality in the image dispersal [44]. In the studies conducted, the results of the train and test processes were not given in detail. In other words, there is no information indicating that the given mAP values are the training or test results. In another study [45] using the Yolov3 and Yolov4 models, mAP values of 84.12% and 88.15% were achieved, respectively. In this study, a very high mAP score was obtained, which also reveals the success of the proposed model. In our study with the Advanced Wildfire Smoke Dataset, the results of the training and test processes were shared in detail, and it was stated that the training mAP value of our improved Yolov5-x model was 97.3%, and the test mAP value was 96.8%. In addition, the original Wildfire Smoke Dataset, which consisted of a single class, was used in the studies. Having a single-class dataset means that the number of objects that the model will detect is limited to one. This means that the model has no possibility of making predictions other than in the specified class. In other words, the possibility of the model giving another class name to the smoke object in the image is eliminated, which may increase the accuracy of the model. Considering these situations, a second class was created by adding images collected from open-source websites to the original Wildfire Smoke Dataset. Thus, it is possible to make more accurate comments regarding the performance of the model.