An efficient fire detection system based on deep neural network for real-time applications

Fires represent an important risk to an entire planet, destroying everything from huge cities to impenetrable forests. This can be prevented using fire detection systems, but have been slow to be implemented due to concerns about the high cost, specialized connection, false alarms, and unreliability of existing facility-based detection systems. This take a first step towards utilizing DL to detect fire in images in this work. A Forest Fires dataset, obtained by an UCI ML Repository, is utilized for both training and testing purposes in this study. The components of preprocessing methods involve defining the paths for training and testing data, converting images to pixel representations, normalizing of the data and target variable selection. The model is applied in place due to its ability to highlight intricate details and patterns that are the main elements of precise fire detection. This research presents new methods for detecting forest fires through the use of a carefully selected dataset, transfer learning using the Hybrid (ResNet152V2 and InceptionV3) model and also deep learning based ConvNext model, and innovative preprocessing procedures. The novelty of this study arises from the effective incorporation of Hybrid (ResNet152V2, InceptionV3) model and ConvNext model into the field of fire detection, demonstrating its capability to attain exceptional levels of accuracy and precision. Techniques of visualization and comprehensive evaluation metrics raise the study's novelty. By utilizing the Hybrid (ResNet152V2 and InceptionV3) model, which attains an astounding accuracy, recall, f1-score, and precision of 99.47%, exceptional performance is achieved. While also ConvNext model get 95.53% accuracy. This study makes a valuable contribution to the field of fire detection systems by utilizing innovative deep neural network architectures to enhance performance and dependability.