FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments
Year: 2024
Type: conference-paper
Abstract: Fire hazards are extremely dangerous, particularly in sectors such the transportation industry where political unrest increases the likelihood of their occurring. 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 answer 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.
Authors: Mahamudul Hasan, Md Maruf Al Hossain Prince, Mohammad Samar Ansari, Sabrina Jahan, Abu Saleh Musa Miah, Jungpil Shin
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411173763