轻量级火情检测模型，3.5万参数实现98.77%准确率
FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments
- 基于迁移学习设计极简卷积网络，适配嵌入式设备
- 仅34,978个可训练参数，准确率达98.77%
- 适合车载摄像头等资源受限场景的实时火情监测
火灾危害极为严重，尤其在运输行业，政治动荡会增加其发生概率。通过在运输车辆上部署基于IP摄像头的火情检测系统，可提前预防火灾损失。然而，由于摄像头内嵌系统计算能力有限，亟需轻量化火情检测模型。为此，我们提出FireLite，一种参数极少的卷积神经网络（CNN），专为资源受限环境设计，实现快速火情检测。该模型仅含34,978个可训练参数，在测试中达到98.77%的准确率，验证损失为8.74，精度、召回率和F1分数均达98.77%。凭借高精度与高效性，FireLite是资源受限环境下火情检测的有力解决方案。
原文摘要 · Abstract (English)
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.
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