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Implementation and Research on Neural Network-Based Monitoring System for Preventing Battery-Related Fire Hazards in Indoor Environments
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Implementation and Research on Neural Network-Based Monitoring System for Preventing Battery-Related Fire Hazards in Indoor Environments
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Implementation and Research on Neural Network-Based Monitoring System for Preventing Battery-Related Fire Hazards in Indoor Environments
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Journal Article
Implementation and Research on Neural Network-Based Monitoring System for Preventing Battery-Related Fire Hazards in Indoor Environments
2026
Overview
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.
