This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification\nsystem with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most\nconsumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire\npresent and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural\nnetwork (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep\nlearning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to\nenable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as\nflames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest,\nusing visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in\nreal-time with location information. The efficiency of the fire detection and notification system employed by standard fire\ndetectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with\ntimely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results\nshowed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.
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