Research on Fire Detection in Laboratories Based on CNN and Transfer Learning

Abstract

To solve the problem that model may not be convergent owing to datasets with small sample sizes results from the secrecy of labs, a fire detection method for lab fire detection based on CNN (Convolutional Neural Network) and transfer leaning is proposed. Experiments of transfer learning were conducted with configuration of super parameters and a small lab fire dataset, whose pre-trained models are GoogLeNet, ResNet and MobileNet trained with ImageNet dataset. We analyze and assess the loss function curve and the performances of the above models. Result shows that MoblieNet model is chosen to be the optimum model for fire detection, providing a reference to lab fire detection with small sample size datasets. Different from artificial fire surveillance, the proposed method based on CNN and transfer learning realize real-time, autonomy and efficient fire detection.