<![CDATA[Wildfires are untamable and devastating forces that impact both urban and rural regions. While predicting wildfires is challenging, efforts are made to mitigate the damage they inflict. The previous researches have limitations such as not being able to find a small region of fire in the dataset. In this research, pairwise region-based convolutional neural network (PR-CNN) is proposed for wildfire detection. The dataset used for wildfire detection is the fire luminosity airborne-based machine learning evaluation (FLAME) dataset that is pre-processed through normalization and hue, saturation, and lightness (HSV) color space to improve the image quality. Pre-processed images are taken as input to region-based convolutional neural network (R-CNN) for detection, the R-CNN has a region proposal layer that is enhanced by pairwise region and named PR-CNN. These wrapped images are fed into CNN architecture to extract and features to detect wildfire. Additionally, post processing technique like soft-non-maximum suppression (NMS) is utilized to eliminate the duplicate detection from PR-CNN for enhancing the detection accuracy. The proposed method achieves a higher accuracy of 97.44%, a precision of 97.32%, recall of 97.31%, and f1-score of 96.67%, which is comparatively superior to the existing algorithms like recurrent neural network (RNN), and R-CNN.]]>
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