A real-time video smoke detection algorithm based on Kalman filter and CNN

Gagliardi, Alessio; Gioia, Francesco de; Saponara, Sergio

doi:10.1007/s11554-021-01094-y

Cited by 25 publications

(11 citation statements)

References 22 publications

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“…In recent years, deep learning-based algorithms, especially CNNs [17][18][19][20][21], have gained so much attention, and many researchers investigated those models and achieved good precision results. Hence, the use of those methods is still challenging since they rely on supervised learning and require huge databases for training, and their effectiveness depends on various factors, including the strong hardware systems for implementation and the quality and diversity of the dataset used for training.…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

Daoudi,

Mouelhi,

Bouchouicha

et al. 2023

Electronics

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In recent decades, the need for advanced systems with good precision, low cost, and high-time response for wildfires and smoke detection and monitoring has become an absolute necessity. In this paper, we propose a novel, fast, and autonomous approach for denoising and tracking smoke in video sequences captured from a camera in motion. The proposed method is based mainly on two stages: the first one is a reconstruction and denoising path with a novel lightweight convolutional autoencoder architecture. The second stage is a specific scheme designated for smoke tracking, and it consists of the following: first, the foreground frames are extracted with the HSV color model and textural features of smoke; second, possible false detections of smoke regions are eliminated with image processing technique and last smoke contours detection is performed with an adaptive nonlinear level set. The obtained experimental results exposed in this paper show the potential of the proposed approach and prove its efficiency in smoke video denoising and tracking with a minimized number of false negative regions and good detection rates.

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Section: Introductionmentioning

confidence: 99%

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

Daoudi,

Mouelhi,

Bouchouicha

et al. 2023

Electronics

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“…Wang [11] proposed an image enhancement method based on fuzzy logic to alleviate the interference of low and uneven lighting in coal mine, and used support vector machine (SVM) to classify smoke by features of perimeter and area ratio, area randomness and drift characteristics. And the convolutional neural network CNN has been widely used in video smoke detection [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Video-based smoke detection by using motion, color, and texture features

Zhao

Cui²,

Li³

et al. 2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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Video smoke detection benefits life safety and environment protection, its early warning is of great importance. In response of many disadvantages of traditional smoke detectors, a method of video smoke detection based on dynamic, color and texture features is proposed. Firstly, the motion area is extracted through improved Vibe algorithm. Then, the suspected area corresponding to smoke is identified in CIELAB color space and segmented using a color filtering method. Finally, uniform local binary mode and gray level cooccurrence matrix are extracted from the image within suspected area, and used to form the input vector of machine learning classifier for recognizing smoke. The classifier is tested with 400 images, and the results show that the detection system based on random forest algorithm has better performance, the selected smoke features have high recognition accuracy.

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“…In recent years, many methods based on convolutional neural networks (CNNs) have attracted attention due to their outstanding performance in image segmentation [33]. Semantic segmentation based on CNN, with the input of an arbitrary-size image, utilizes a set of convolutional layers, non-linear activation functions, pooling and upsampling layers to output a predicted image [34][35][36][37][38]. Moreover, CNNs have achieved a lot of significant results in the field of vision detection of forest fire smoke [39,40].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Wang

Zheng

Yin³

et al. 2021

Electronics

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Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.

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A real-time video smoke detection algorithm based on Kalman filter and CNN

Cited by 25 publications

References 22 publications

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

Video-based smoke detection by using motion, color, and texture features

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

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