Fire Detection by using DenseNet 201 algorithm and Surveillance Cameras images

Early warning of fires is very important to reduce loss of life and property. It is a technology that has become one of the most important design elements for smart cities in the future. Most current solutions rely on temperature and smoke sensors, which have limited detection ranges and scenarios and long response times. These are used inside some buildings and cannot be used in the external environment, as they are not a preferred solution. In addition to the above, sensor systems are costly because of the hardware and installation requirements, and they require regular upkeep. With the development of artificial intelligence and learning machines, fire detection has been used based on deep learning. Widely. Therefore, a solution to computer vision technology is proposed, which uses a convolutional neural network (CNN), Reducing false alarms. The use of computer vision techniques is certainly better than sensor-based systems, by utilizing security camera footage at specific periods to detect a fire when it breaks out. Our system achieved excellent results with an average predictive accuracy of 98% on the dataset. This accuracy and low cost make it a good alternative to systems that use sensors. The contribution of this study is to improve the detection process by conducting a classification process instead of detecting an object (fire), which requires complex mathematical calculations, large resources, and a long time.