Detection of Fire, non-fire and smoke using combination CNN and Transfer Learning

In the study, over Fire and Smoke Detection system by application of Convolutional Neural Networks (CNN) is presented by using a dataset comprising 3,500 images per class, namely fire, smoke, and non-fire, for training as well as for testing. The dataset was separated into training, validation, and testing subsets, with images being labelled as ‘fire’, ‘smoke’, or ‘non-fire’. A Convolutional Neural Networks (CNN) model utilizing a frozen ResNet50 architecture was trained using TensorFlow on Google Colab as well as on local system. The proposed model achieved an overall accuracy of 80.58% and an overall AUC score of 0.9429, illustrating a satisfactory generalization capability. The final trained model was evaluated on the test dataset. Additionally, the model was utilized for real time detection using Python and OpenCV, enabling the processing of camera inputs. At testing, the system successfully detected the events. In addition, the system can be configured to send alert notifications upon detection, making it suitable for early warning and prevention in domestic and industrial environments.