Surveillance, Fire Detection and Protection System for Substations

Abstract

Fire detection is most important in substations, due to its dangerous nature. It is necessary to detect fire early with accuracy. There comes the need of an accurate system for fire detection. So with the help of Neural Network we can detect fire from a surveillance video. The older sensor based systems and image processing based methods are not accurate and they often give false alarms due to wrong detection in outdoor areas. They need high implementation cost and not easy to cover larger areas. The environmental changes can also affect them. They have low performance and cause false alarms while there are shadows, varying lights and fire coloured objects. Neural Network helps to improve the fire detection capability with increased accuracy. It can detect fire in multiple situations as like day and night. To provide a proper fire detection in substation yard, Neural network based fire detection is implemented. This system consists of a video camera which takes live footages of the area. The video is processed by the Neural Network on the basis of fire detection algorithm. The network is trained with different models and different settings to understand the nature of fire which is similar to the GoogLeNet. Various images of fire are given as input to train the network using ImageNet. In order to prevent false alarm fire like substances are taught to the network, which improves accuracy. As the no: of image sets used for training the network increases, the accuracy of the networks increases. Once the network is trained it will be able to detect fire and give alerts accurately. Compared to traditional fire detection methods neural networks provide accurate and fast response to fire in both indoor and outdoor conditions.