Full-Scale Video-Based Detection of Smoke from Forest Fires Combining ViBe and MSER Algorithms

Chen

et al. 2022

Self Cite

Smoke is an early visual phenomenon of forest fires, and the timely detection of smoke is of great significance for early warning systems. However, most existing smoke detection algorithms have varying levels of accuracy over different distances. This paper proposes a new smoke root detection algorithm that integrates the static and dynamic features of smoke and detects the final smoke root based on clustering and the circumcircle. Compared with the existing methods, the newly developed method has a higher accuracy and detection efficiency on the full scale, indicating that the method has a wider range of applications in the quicker detection of smoke in forests and the prevention of potential forest fire spread.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Full-Scale Fire Smoke Root Detection Based on Connected Particles

Feng

Chen

et al. 2022

Self Cite

“…In the literature, ref. [21] combines the ViBe algorithm and the MSER algorithm through Bayesian theory to form a better shape of the smoke candidate region, which is used for complete and full-scale video smoke detection. However, a single static feature is usually greatly affected by the environment, and it is difficult to describe the overall characteristics of smoke well.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Sun

et al. 2022

Self Cite

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.

“…The smoke sensors detect the physicochemical properties of smoke, which is challenging for applications in open forest monitoring scenarios and cannot distinguish between the different smoke classes produced by forest fire or a non-hazardous causes. Visual cameras record images [ 10 ] or videos [ 11 , 12 , 13 , 14 , 15 ], and the smoke detection is performed by feature extraction to identify, locate, or segment smoke in the recorded images or videos.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Huang

2022

Self Cite

To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification.