Abstract

Forests are an essential natural resource for humans, offering a plethora of direct and indirect advantages. The existence of life on Earth and the increasing effects of global warming are significantly impacted by forest fires and other natural disasters. Therefore, automatic forest fire detection is a crucial area of research for disaster prevention. Early detection plays a vital role in strategizing fire suppression efforts and mitigating potential damage. Wildfires exhibit diverse shapes, textures, and colors, making them challenging to detect using standard feature extraction techniques. This study examined the application of computer vision algorithms based on AI for smoke and fire detection in images. Convolutional neural networks (CNNs) demonstrated superior performance over traditional computer vision techniques, such as image classification. However, despite their effectiveness, CNNs required extensive training time, posing computational challenges in real-time fire detection scenarios. To solve this issue, a new hybrid visual geometry group 16 (VGG-16) layer network and the You Only Look Once Version 5 (YOLOv5) model are used for video-based fire detection. The image classifier VGG-16 is utilized in this instance to identify and categorize whether fire is present in the input data. From the categorized output of the VGG classifier, the object detector YOLOv5, is then applied to detect fire. A special effort has also been made to compile and preprocess the dataset, which includes images of fires and non-combustible objects, with as many real-world representative situations as possible. The test results demonstrated that the YOLOv5 detection algorithm has an accuracy of 70% and the VGG image classifier has an accuracy of more than 90%. The accuracy of YOLOv5 was greatly improved to 84% with reduced training time after the hybrid VGG-16-YOLOv5 model was developed. False positives (FP) were also reduced because background mistakes were removed even in videos containing smoke. According to experimental results, the suggested models perform better than the state-of-the-art techniques currently in use.

Keywords

Forest fire detection, Computer vision, Convolutional neural networks (CNNs), VGG-16-YOLOv5 hybrid model, Real-Time fire detection.

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