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.

翻译：火灾危害极其危险，在交通运输业等政治动荡加剧其发生可能性的行业中尤为如此。通过采用IP摄像头在运输车辆上便捷部署火灾检测系统，可以主动预防火灾事件造成的损失。然而，由于这些摄像头内嵌系统的计算能力受限，需要开发轻量级火灾检测模型。针对这一挑战，我们提出了FireLite——一种专为资源受限环境下快速火灾检测设计的低参数量卷积神经网络（CNN）。我们的模型仅包含34,978个可训练参数，却取得了卓越的性能指标：准确率达到98.77%，验证损失为8.74，精确率、召回率和F1分数均达到98.77的峰值。凭借其精确性与高效性，FireLite为资源受限环境下的火灾检测提供了极具前景的解决方案。