An Efficient Smoke Detection Algorithm Based on Deep Belief Network Classifier Using Energy and Intensity Features

Smoke detection plays an important role in forest safety warning systems and fire prevention. Complicated changes in the shape, texture, and color of smoke remain a substantial challenge to identify smoke in a given image. In this paper, a new algorithm using the deep belief network (DBN) is designed for smoke detection. Unlike popular deep convolutional networks (e.g., Alex-Net, VGG-Net, Res-Net, Dense-Net, and the denoising convolution neural network (DNCNN), specifically devoted to detecting smoke), our proposed end-to-end network is mainly based on DBN. Indeed, most traditional smoke detection algorithms follow the pattern recognition process which consists basically feature extraction and classification. After extracting the candidate regions, the main idea is to perform both smoke recognition and smoke-no-smoke region classification using static and dynamic smoke characteristics. However, manual smoke detection cannot meet the requirements of a high smoke detection rate and has a long processing time. The convolutional neural network (CNN)-based smoke detection methods are significantly slower due to the maxpooling operation. In addition, the training phase can take a lot of time if the computer is not equipped with a powerful graphics processing unit (GPU). Thus, the contribution of this work is the development of a preprocessing step including a new combination of features—smoke color, smoke motion, and energy—to extract the regions of interest which are inserted within a simple architecture with the deep belief network (DBN). Our proposed method is able to classify and localize reliably the smoke regions providing an interesting computation time and improved performance metrics. First, the Gaussian mixture model (GMM) is employed to capture the frames containing a large amount of motion. After applying RGB rules to smoke pixels and analyzing the energy attitude of smoke regions, extracted features are then used to feed a DBN for classification. Experimental results conducted on the publicly available smoke detection database confirm that the DBN has reached a high detection rate that exceeded an average of 96% when tested on different videos containing smoke-like objects, which make smoke recognition more challenging. The proposed methodology provided high detection ratios and low false alarms, and guaranteed robustness verified by evaluations of accuracy, F1-score, and recall for noisy and non-noisy images with and without noise.

Smoke recognition network based on dynamic characteristics

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.