School of Technology, Beijing Forestry University, Beijing 100083, China.
Department of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA.
Sensors (Basel). 2022 Jun 20;22(12):4655. doi: 10.3390/s22124655.
The advances in developing more accurate and fast smoke detection algorithms increase the need for computation in smoke detection, which demands the involvement of personal computers or workstations. Better detection results require a more complex network structure of the smoke detection algorithms and higher hardware configuration, which disqualify them as lightweight portable smoke detection for high detection efficiency. To solve this challenge, this paper designs a lightweight portable remote smoke front-end perception platform based on the Raspberry Pi under Linux operating system. The platform has four modules including a source video input module, a target detection module, a display module, and an alarm module. The training images from the public data sets will be used to train a cascade classifier characterized by Local Binary Pattern (LBP) using the Adaboost algorithm in OpenCV. Then the classifier will be used to detect the smoke target in the following video stream and the detected results will be dynamically displayed in the display module in real-time. If smoke is detected, warning messages will be sent to users by the alarm module in the platform for real-time monitoring and warning on the scene. Case studies showed that the developed system platform has strong robustness under the test datasets with high detection accuracy. As the designed platform is portable without the involvement of a personal computer and can efficiently detect smoke in real-time, it provides a potential affordable lightweight smoke detection option for forest fire monitoring in practice.
开发更准确、快速的烟雾检测算法的进展增加了烟雾检测中的计算需求，这需要个人计算机或工作站的参与。更好的检测结果需要更复杂的烟雾检测算法的网络结构和更高的硬件配置，这使得它们不适合作为用于高效检测的轻便便携式烟雾检测。为了解决这个挑战，本文基于 Linux 操作系统设计了一个基于 Raspberry Pi 的轻便便携式远程烟雾前端感知平台。该平台有四个模块，包括视频源输入模块、目标检测模块、显示模块和报警模块。使用公共数据集的训练图像，使用 OpenCV 中的 Adaboost 算法对基于局部二值模式（LBP）的级联分类器进行训练。然后，该分类器将用于检测以下视频流中的烟雾目标，并在实时显示模块中动态显示检测结果。如果检测到烟雾，平台中的报警模块将向用户发送警告消息，以便对现场进行实时监控和报警。案例研究表明，该开发系统平台在具有高检测精度的测试数据集下具有很强的鲁棒性。由于所设计的平台是便携式的，不涉及个人计算机，并且可以实时高效地检测烟雾，因此为实际的森林火灾监测提供了一种潜在的经济实惠的轻便烟雾检测选择。
