An Embedded Portable Lightweight Platform for Real-Time Early Smoke Detection

Abstract

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.

AI-Enhanced Gas Flares Remote Sensing and Visual Inspection: Trends and Challenges

“…In terms of early-stage smoke detection, another issue to be considered, again, is the required quick time response for the real-time video processing problem. Liu et al [50] achieved this goal in an embedded platform by using a cascaded structure of AdaBoost classifiers together with some techniques such as Local Binary Pattern (LBP), histogram equalization, and image denoising (by Gaussian filter). They highlight that the smoke detection results can be derived by a lightweight device and achieve real-time monitoring of potential smoke.…”

Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning

“…The traditional visual detection method directly uses artificially designed features. For example, the commonly used feature descriptions in the mainstream traditional image-based smoke detection methods [ 16 , 17 ] include color, texture, edge and other features. These cannot be used to distinguish different types of smoke, so the traditional image-based smoke detection method cannot avoid the false positive issues when different classes of smoke are present in the detection scenes.…”