Implementation and Research on Neural Network-Based Monitoring System for Preventing Battery-Related Fire Hazards in Indoor Environments
DOI:
https://doi.org/10.4108/ew.11622Keywords:
openMV, STM32, microcontroller, deep learning, neural networkAbstract
With the widespread use of electric bicycles and batteries, fire accidents caused by batteries have become increasingly serious, especially in closed indoor environments. Traditional fire prevention methods often rely on static monitoring and simple sensors, which can suffer from delayed responses or fail to accurately identify fire hazards. To address this issue, this paper proposes an innovative monitoring system for preventing fire hazards caused by batteries or electric bicycles in indoor environments, based on an STMicroelectronics 32-bit Microcontroller (STM32) and Open-source Machine Vision module (OpenMV). The system uses deep learning to train a neural network to recognize the image information of batteries or electric bicycles. The key innovation of this system lies in several aspects: firstly, it utilizes the OpenMV module for real-time image processing, enabling efficient and accurate recognition of batteries and electric bicycles; secondly, the integration with the STM32 microcontroller enhances the system’s data processing capabilities and enables flexible communication and responses with external devices; finally, the system features high-efficiency serial communication, ensuring the real-time transmission and processing of monitoring data for swift responses to potential fire risks. Experimental results show that the system can accurately identify batteries or electric bicycles in indoor environments and respond in a timely manner, significantly reducing fire hazards. In addition, the system's design is not limited to preventing battery-related fire hazards in indoor environments. Compared to traditional methods, this study's innovation lies in combining deep learning and embedded control technology for fire prevention, providing a practical and scalable solution for battery-related fire risk prevention.
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[1]Spotnitz, R., Franklin, J. Abuse behavior of high-power, lithium-ion cells. Journal of Power Sources, 2003, 113(1): 81–100.
[2]Doughty, D. H., Roth, E. P. A general discussion of Li-ion battery safety. The Electrochemical Society Interface, 2012, 21(2): 37–44.
[3]Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., He, X. Thermal runaway mechanism of lithium-ion battery for electric vehicles: A review. Energy Storage Materials, 2018, 10: 246–267.
[4]Celik, T., Demirel, H. Fire detection in video sequences using a generic color model. Fire Safety Journal, 2009, 44(2): 147–158.
[5]Ko, B. C., Cheong, K. H., Nam, J. Y. Early fire detection algorithm based on smoke and flame color models. Fire Safety Journal, 2009, 44(3): 322–329.
[6]Toreyin, B. U., Dedeoglu, Y., Cetin, A. E. Wavelet based real-time smoke detection in video. Proc. ICIP, 2005: 213–216.
[7]Howard, A., Sandler, M., Chu, G., et al. Searching for MobileNetV3. Proc. ICCV, 2019: 1314–1324.
[8]Howard, A. G., Zhu, M., Chen, B., et al. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861, 2017.
[9]Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C. MobileNetV2: Inverted residuals and linear bottlenecks. Proc. CVPR, 2018: 4510–4520.
[10]Maxim Integrated. DS18B20 Programmable Resolution 1-Wire Digital Thermometer. Rev 5, 2015.
[11]Han, S., Mao, H., Dally, W. J. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. Proc. ICLR, 2016.
[12]National Fire Protection Association. NFPA 72: National Fire Alarm and Signaling Code. 2019 Edition.
[13]Shorten, C., Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. Journal of Big Data, 2019, 6: 60.
[14]STMicroelectronics. VL53L0X Time-of-Flight Ranging Sensor Datasheet. Rev 2, 2016.
[15]Jacob, B., Kligys, S., Chen, B., et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference. Proc. CVPR, 2018: 2704–2713.
[16]Reddi, V. J., Cheng, C., Cheng, Y., et al. MLPerf Tiny benchmark. arXiv:2106.07597, 2021.
[17]Han, S., Mao, H., Dally, W. J. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. Proc. ICLR, 2016.
[18]Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A. XNOR-Net: ImageNet classification using binary convolutional neural networks. Proc. ECCV, 2016: 525–542.
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Copyright (c) 2026 Xin Li, Hanyu Shi, Shiqi Zhou
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