FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments
This addresses fire hazards in sectors like transportation with limited computational resources, though it is incremental as it adapts existing methods to a specific domain.
The paper tackles efficient fire detection for resource-constrained environments like transport vehicles by introducing FireLite, a lightweight CNN with 34,978 parameters that achieves 98.77% accuracy and high precision, recall, and F1-scores.
Fire hazards are extremely dangerous, particularly in sectors such as the transportation industry, where political unrest increases the likelihood of their occurrence. By employing IP cameras to facilitate the setup of fire detection systems on transport vehicles, losses from fire events may be prevented proactively. However, the development of lightweight fire detection models is required due to the computational constraints of the embedded systems within these cameras. We introduce FireLite, a low-parameter convolutional neural network (CNN) designed for quick fire detection in contexts with limited resources, in response to this difficulty. With an accuracy of 98.77\%, our model -- which has just 34,978 trainable parameters achieves remarkable performance numbers. It also shows a validation loss of 8.74 and peaks at 98.77 for precision, recall, and F1-score measures. Because of its precision and efficiency, FireLite is a promising solution for fire detection in resource-constrained environments.