Fire Sensor and Surveillance Camera-Based GTCNN for Fire Detection System

Fire accident is a disaster that can happen anytime anywhere due to accidental causes. In existing works, sensor- and computer vision-based approaches have been used for developing the fire detection model, but they fail to attain the accurate results. The sensor-based methods need more time to detect the fire locations and detection coverage also less. The camera sometimes will consider heavy sunlight as fire and it leads to false positive result, which degrades the accuracy. To overcome the above problems, in this research, a novel optimized Gaussian probability-based threshold convolutional neural network (GTCNN) model has been proposed for detecting the fire accidents using various sensors and surveillance camera-based video (SV). Sensor features map has been calculated from various fire sensors and frames/images from SV are preprocessed using a multiscale retinex algorithm. In addition, the Gaussian threshold (GT) logically integrates with the feature map to increase fire pixel count in low-resolution images. The probability results from sensors and SV camera are optimized by multiobjective mayfly optimization (MOMO) algorithm that normalizes the network parameters, which gives the accurate result. The performance of the proposed optimized GTCNN net is different from the existing deep learning networks in terms of multifeature processing. The result of the proposed work attains the detection accuracy of 98.23%. The proposed optimized GTCNN improves the overall accuracy of 3.25%, 3.79%, and 0.21% better than the channel attention mechanism, lightweight CNN, and you only look once (YOLOv5m), respectively.