Abstract:
In this paper, a video processing based forest fire
and smoke detection using a Fuzzy Entropy optimized thresholding and convolutional neural network (CNN) based model is
proposed. In our proposed architecture adopts and introduces
a spatial transformer network (STN) in the CNN layer and
entropy function thresholding operation in the softmax layer
due to addition in the softmax layer it works excellently and
accurately. We have taken initial preprocessing video quality
as an essential issue, which helps us in providing less false
alarm rate, we checked the blockiness, blurriness, noisiness
and used efficient ways to enrich the quality of the video. After
preprocessing, we propose the Three Frame Difference Method
to calculate the motion of all frames. During feature extraction,
we suggest an STN-Based CNN extract the dynamic features of
an image. After extraction, we need to perform the classification
step, and here we introduce the entropy function thresholding
method in the softmax layer. The results obtained with our
proposed architecture were compared with the existing methods
in the literature and showed a higher exact fire detection rate, a
very less false detection rate, good accuracy, and less execution
time in terms of complexity. The proposed method can be
used efficiently for real-time forest fire detection with a moving
camera.
