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

Abstract

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.

Doing More With Moiré Pattern Detection in Digital Photos

“…However, moiré patterns in the generated layers are either too faint or contain a great deal of noise since these approaches tend to produce distorted samples that are not well-aligned with their original images. In terms of detected patterns, it is somewhat similar to translucent pattern detection scenarios like smoke [18], motion blur [19], shadow [20], haze [21], specular highlights [22] and raindrops [23], etc. However, these approaches cannot be directly employed because unlike the aforementioned scenarios, moiré effects are highly variable and dependent on the intensity and shape of fine-grained moiré patterns.…”

Robust Image Classification with Cognitive-Driven Color Priors

“…Since AlexNet [15,16] won the ILSVRC competition, convolutional neural networks have become the mainstream method for image classification, more and more classical networks continue to spring up [17][18][19]. GoogLeNet [20] propose the inception structure and increases the diversity of features.…”

A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

“…Some scholars have introduced CNN models-for example, AlexNet [15], GoogleNet [16], ZF-Net [17], VGG [18], and ResNet [19], into the field of the vision detection of fires. Regarding the use of these models, some scholars have also proposed improved CNN-based methods for fire or smoke detection, such as, smoke detection in a video based on a deep belief network using energy and intensity features [20], a video-based detection system using an object segmentation and efficient symmetrical features [21], a two-stream CNN model with the adaptive adjustment of the receptive field [22], and an object detection model incorporating environmental information [23]. Additionally, Liu et al [24] also proposed a forest fire detection system based on ensemble learning to reduce false alarms.…”