Kok Chiang Liang, Heng Jovan Bowen, Koh Yit Yan, Teo Tee Hui
College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia.
Engineering Product Development, Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore.
Sensors (Basel). 2025 Mar 12;25(6):1761. doi: 10.3390/s25061761.
Hazard detection in industrial and public environments is critical for ensuring safety and regulatory compliance. This paper presents an energy-efficient, cost-effective IoT-based hazard detection system utilizing an ESP32-CAM microcontroller integrated with temperature (DHT22) and motion (PIR) sensors. A custom-built convolutional neural network (CNN) deployed on a Flask server enabled real-time classification of hazard signs, including "high voltage", "radioactive", "corrosive", "flammable", "no hazard", "no smoking", and "wear gloves". The CNN model, optimized for embedded applications, achieves high classification accuracy with an F1 score of 85.9%, ensuring reliable detection in diverse environmental conditions. A key feature of the system is its adaptive monitoring mechanism, which dynamically adjusts image capture frequency based on detected activity, leading to 31-37% energy savings compared to continuous monitoring approaches. This mechanism ensures efficient power usage by minimizing redundant image captures while maintaining real-time responsiveness in high-activity scenarios. Unlike traditional surveillance systems, which rely on high-cost infrastructure, centralized monitoring, and subscription-based alerting mechanisms, the proposed system operates at a total cost of SGD 38.60 (~USD 28.50) per unit and leverages free Telegram notifications for real-time alerts. The system was validated through experimental testing, demonstrating high classification accuracy, energy efficiency, and cost-effectiveness. In this study, a hazard refers to any environmental condition or object that poses a potential safety risk, including electrical hazards, chemical spills, fire outbreaks, and industrial dangers. The proposed system provides a scalable and adaptable solution for hazard detection in resource-constrained environments, such as construction sites, industrial facilities, and remote locations. The proposed approach effectively balances accuracy, real-time responsiveness, and low-power operation, making it suitable for large-scale deployment.
在工业和公共环境中进行危险检测对于确保安全和遵守法规至关重要。本文介绍了一种基于物联网的节能、经济高效的危险检测系统，该系统利用集成了温度（DHT22）和运动（PIR）传感器的ESP32-CAM微控制器。部署在Flask服务器上的定制卷积神经网络（CNN）能够对危险标志进行实时分类，包括“高压”、“放射性”、“腐蚀性”、“易燃”、“无危险”、“禁止吸烟”和“戴手套”。针对嵌入式应用进行优化的CNN模型实现了85.9%的F1分数，具有较高的分类准确率，确保在各种环境条件下都能可靠检测。该系统的一个关键特性是其自适应监测机制，它根据检测到的活动动态调整图像捕获频率，与连续监测方法相比，可节省31-37%的能源。这种机制通过最小化冗余图像捕获来确保高效的电力使用，同时在高活动场景中保持实时响应能力。与依赖高成本基础设施、集中监控和基于订阅的警报机制的传统监控系统不同，该系统的单位总成本为38.60新元（约合28.50美元），并利用免费的Telegram通知进行实时警报。该系统通过实验测试得到验证，证明了其高分类准确率、能源效率和成本效益。在本研究中，危险是指任何构成潜在安全风险的环境条件或物体，包括电气危险、化学品泄漏、火灾爆发和工业危险。该系统为资源受限环境（如建筑工地、工业设施和偏远地区）中的危险检测提供了一种可扩展且适应性强的解决方案。所提出的方法有效地平衡了准确性、实时响应能力和低功耗运行，使其适合大规模部署。
