Vision Sensor Assisted Fire Detection in IoT Environment using ConvNext

S. Zahir, W. Abbas, R. Khan, M. Ullah, Arbab Waseem, Rafi Ullah Abbas, M. Khan
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Abstract

To mitigate social, ecological, and financial damage, effective fire detection and control are crucial. Performing real-time fire detection in Internet of Things (IoT) environments, however, presents significant challenges due to limited storage, transmission, and computational resources. Early fire detection and automated response are essential for addressing these challenges. In this paper, we introduce an IoT-supported deep learning model designed for efficient fire detection. The proposed model builds upon the pre-trained weights of the ConvNext convolutional neural network, which excels at detecting minute features and distinguishing between yellow lights and fire patterns. Implemented on an IoT device, the system triggers an alert when a fire is detected, prompting necessary actions. Our method, tested on the forest fire dataset, demonstrated a 4% improvement in accuracy compared to existing deep learning models for fire detection.
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视觉传感器在物联网环境下使用ConvNext辅助火灾探测
为了减轻社会、生态和经济损失,有效的火灾探测和控制至关重要。然而,由于存储、传输和计算资源有限,在物联网(IoT)环境中执行实时火灾探测面临重大挑战。早期火灾探测和自动响应对于应对这些挑战至关重要。在本文中,我们介绍了一种物联网支持的深度学习模型,旨在实现高效的火灾探测。该模型建立在预先训练的卷积神经网络的权重上,该网络擅长于检测微小特征和区分黄色灯光和火焰模式。该系统在物联网设备上实现,当检测到火灾时触发警报,提示必要的操作。我们的方法在森林火灾数据集上进行了测试,与现有的火灾探测深度学习模型相比,准确率提高了4%。
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