Deep learning for monitoring forest fires, Aïn Mimoune forest application

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2022
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In recent years, major fires with devastating consequences have occurred in various regions of Algeria. The region of the wilaya of Khenchela was affected by heat waves between July and August which caused major fires in several forests, precisely in the region of Ain Mimoune. This thesis addresses the problem of fire and smoke detection in videos, with the aim of early detection of fires in the forests of Aïn Mimoune. As we know current methods use traditional image processing methods that rely primarily on human-made extracted features, which aren’t always appropriate to all forest environments. To solve this issue, we have proposed a custom model based on deep learning techniques that helps in early fire and smoke detectors, the final proposed model integrates two independent models. The first one uses the features extraction power of VGG16 to avoid false positives, while the second one uses YOLOV5 that highlight fire or smoke with a surrounding bounding box. The proposed solution takes into consideration the time and GPU resources issues, so it could be applicable in real-world scenarios. To test our proposed solution on a forest-similar environment, we have created a forestsimulation environment using the Unity3D game engine, which gave us good results even with a lack of GPU resources, this step has opened a big door for future perspectives and suggestions that can be done concerning the fire detection problem. In the end, to make our solution public and available to everyone, we have used the Django framework to create a web application interface that uses the final proposed algorithm.
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