Developing a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network

Abstract

The object of research is a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network. Within the recent few decades, fires are considered as one of the most serious disaster that occurs in many places around the world. The severity of fire incidents causes damages to buildings, infrastructures and properties. Resulting losses of human’s life and costs them a lot of losses. Thus, fire poses a great threat to us significantly; it is extremely dangerous for fire fighters. Fires can be resulted by materials such as rubber and chemical products. Other sources of fire are the short circuits on electrical devices and faults in power circuits. Additionally, overheating and overloading problems can be the cause of fire incidents. All these reasons lead to bad consequences when there is no immediate response to such problems. The advent of computer vision technology has played such a significant role for human life. Artificial intelligence field has improved the efficiency and behaviors of robotics beyond expectations. The interference of artificial intelligence made robotics act intelligently. For this reason, in this paper we presented a mobile robot based on deep learning to detect the fire source and determines its coordinate position then automatically moves toward the target and extinguish fire. Deep learning algorithms are the efficient ones for object detection applications. CNN model is one of the most common deep learning algorithms which have been used in the study for the fire detection. Due to the insufficient amount of datasets and large efforts required to build model from scratch. MobileNet V2 is one of the CNN models that support transfer learning technique. After training the model and testing it on 20 % of the used datasets the classification accuracy achieved up to 98.01 %. The motion repeatability of the robot has been implemented and tested resulting mean error 0.648 cm.