# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 6 Conclusions  
In this study, a method with high accuracy for the early diagnosis and detection of automatic forest fires, based on deep learning, was investigated. This study also aimed to present an improved model for the early detection of forest fires. First, an advanced two-class dataset was presented by adding additional images to the image data of the Wildfire Smoke Dataset, which is an open-source dataset. Subsequently, a model was proposed by replacing the backbone structure of the original Yolov5-x model for ResNet50. A total of 30 different object detection models were comparatively evaluated on the dataset developed within the scope of this study, and it was shown that the proposed model gave the best results, with 96.9% mAP success in the testing phase. In this respect, this study brings a new method to the literature of data processing and modeling techniques that can be used for fire detection; at the same time, it strongly demonstrates the potential of deep learning in preventing environmental disasters. In addition, to verify the success of the proposed model against other models, it was compared with 19 Yolov5 models with a modified backbone structure, five original Yolov5 models, and five Yolov8 models, the most current version of Yolo. It was observed that accuracy rate of the proposed model was higher than that of all other compared models. Our model, which was developed based on the Yolov5-x model, showed better performance with less processing power than the original Yolov5-x model. In addition, our study was compared with other studies using the Wildfire Smoke Dataset, and better results were obtained compared to the studies performed. As a result, smoke detection was successfully performed for the early detection of forest fires. Despite these successes, some factors limit the applicability of the proposed model in different areas and images. The ability of the model to distinguish between phenomena may weaken in dark, hazy, rainy, and smoggy weather. In this context, in future studies, the ability to predict the presence of smoke will be increased by integrating different image processing and smoke detection techniques into the system to eliminate this problem. In future studies, the optimization processes of the proposed model will continue, and the effects on the accuracy will be investigated by performing data augmentation. In addition, the shape and size of the smoke were identified, and fire was detected faster. At the same time, the fire detection process could be accelerated by instantly monitoring the data obtained through instant imaging with drones. The results obtained will be transferred to a mobile system and a prototype of real application software with both mechanical and temporal process control will be created. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All data included in this study are available upon request by contacting the corresponding author or can be downloaded from [[https://doi.org/10.5281/zenodo.14218778](https://doi.org/10.5281/zenodo.14218778)]([https://doi.org/10.5281/zenodo.14218778](https://doi.org/10.5281/zenodo.14218778)) (accessed on 25 November 2024). **Conflicts of Interest:** The author declares that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. The author declares no conflicts of interest related to this article.