Fire detection by using CNN Alex net Algorithm


  • College of Education for Pure Sciences, Computer science department, Thi-Qar University, IRAQ
  • College of Education for Pure Sciences, Computer science department, Thi-Qar University, IRAQ


Fire detection using convolutional neural network(CNN) algorithm and surveillance cameras is a field
that aims to use technology to detect and intervene in fire incidents quickly and effectively. Fires are considered one of the most dangerous disasters that can occur in buildings and facilities, so developing early fire detection systems is vital to preserve lives and property. Surveillance cameras are used to collect real-time images and videos and send them to a fire analysis and detection system. . In the event of a fire being detected, an immediate alert will be issued to the competent authorities or building owners to take the necessary measures. Develop a fire detection system using by CNN-based algorithm. This system must be accurate and cost-effective. It has many advantages use of visibility infrastructure
compared to other existing systems. There are three types.
First: There is no need to update the stove structure, provided that the place is equipped with surveillance
cameras that monitor fire situations and cover the entire place.
Second: camera-based systems provide the actual location, that is, a complete map of the fire location,
which is good , helps in detecting the fire.
Third: the methods used can be highly applicable Watching fires in public places.
Our system achieved excellent results with average prediction accuracy of 98.14% and 98% on the Forest
dataset and the Local dataset, respectively. AlexNet is well known as a transfer learning model, where
knowledge is learned by training a large amount of datasets . AlexNet is a deeper and broader CNN
model introduced in 2012. AlexNet is primarily design , Alex Net consists of five convolutional layers
and three FC layers. The Alex net structure is shown. The first convolutional layer implemented convolution and max pooling using local response normalization (LRN)Finally, two FC layers are used with dropout followed by a SoftMax layer following the first two convolutional layers Overlapping Max Pooling layers The third, fourth and fifth convolutional layers are directly connected. The fifth convolutional layer is followed by the Overlapping Max Pooling layer, whose output is transmitted to a series of two FC layers. The second fully connected layer feeds into a softmax classifie

After all the convolution and FC layers, ReLU nonlinearity is applied. The ReLU nonlinearity of the first and second convolution layers follows a local regularization step before pooling