Smoke Object Segmentation and the Dynamic Growth Feature Model for Video-Based Smoke Detection Systems

Islam, Rashedul; Amiruzzaman, Md; Nasim, Shahriar; Shin, Jungpil

doi:10.3390/sym12071075

Cited by 25 publications

(13 citation statements)

References 16 publications

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“…Ye et al [ 49 ] utilized the common motion characteristics of fire and smoke to identify the smoke. At the same time, Islam et al [ 50 ] used color and motion to identify smoke using a combination of Gaussian mixture model (GMM)-based adaptive moving object detection and an SVM classifier. Their method achieved 97.3% accuracy but did not help detect accidental fires beyond the range of surveillance cameras.…”

Section: Related Workmentioning

confidence: 99%

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

Mukhiddinov

Abdusalomov

2022

Sensors

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Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset.

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Section: Related Workmentioning

confidence: 99%

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

Mukhiddinov

Abdusalomov

2022

Sensors

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“…The σ used here to determine the width of the kernel function k. Note that, if σ values are small, then overtraining may occur. Again, if σ values are large, then the basis function puts an oval around the points without describing their shapes or patterns [36].…”

Section: User Authentication Using Classification Algorithmmentioning

confidence: 99%

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

et al. 2021

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Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

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“…Traditional machine learning methods are support vector machine (SVM) [18], fuzzy neural networks [19], smoke segmentation-based local binary pattern Silhouettes coefficient variant (LSPSCV) [20], color segmentation-based radial basis function (RBF) nonlinear Gaussian kernel-based binary SVM [21], color segmentation-based fuzzy model [22], and fire frame segmentation-based Markov random field [23]. However, most existing studies on fire detection train models using smoke or flame from videos are often difficult to track more complicated smoke situations.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Image Feature‐Based Deep Belief Network for Fire Detection

et al. 2021

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Detecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. In this paper, a statistic image feature-based deep belief network (DBN) is proposed for fire detections. Firstly, for each individual image, all the statistic image features extracted from a flame and smoke image in time domain, frequency domain, and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBN to classify the multiple fire stages in complex environment. DBN can automatically learn fault features layer by layer using restricted Boltzmann machine (RBM). Experiments using the benchmark data of three groups of fire and fire-like images are classified by the present method, and the classification results are also compared with those commonly used support vector machine (SVM) and convolutional deep belief networks (CDBNs) to manifest the superiority of the classification accuracy.

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Smoke Object Segmentation and the Dynamic Growth Feature Model for Video-Based Smoke Detection Systems

Cited by 25 publications

References 16 publications

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

A Statistical Image Feature‐Based Deep Belief Network for Fire Detection

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