System to detect fire under surveillanced area

Jayashree, D.; Pavithra, S.; Vaishali, G.; Vidhya, J.

doi:10.1109/iconstem.2017.8261285

Cited by 10 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Foggia et al applied motion analysis, shape variation, color features, and bag-of-word for fire classification [ 27 ]. Existing methods also applied gray level co-occurrence matrix and histogram of oriented gradient with SVM [ 28 ], background subtraction, and color space selection for candidate fire region extraction [ 29 ]. In the TFD-based methods, handcrafted features extraction is a very tedious and time-consuming process, and these methods failed to achieve a high-level of accuracy.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Yar

Hussain

Khan

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource-constrained devices. The internal architecture of the proposed model is inspired by the block-wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small-size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia’s dataset and our newly created small-scaled fire detection dataset with extremely challenging real-world images containing a high-level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state-of-the-art in terms of accuracy, false-positive rate, model size, and running time, which indicates its robustness and feasible installation in real-world scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Yar

Hussain

Khan

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Moreover, in another study, authors used mobility assessment, shape diversity, color characteristics, and bag-of-word for classifying fires [27]. Antecedent methods also used a gray-level co-occurrence matrix and an oriented gradient histogram in combination with SVM [28]. In TFD-based approaches, manually crafted feature extraction is a complicated and time-intensive task, and these approaches are unable to accomplish a high level of precision.…”

Section: Related Workmentioning

confidence: 99%

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Nadeem,

Dilshad,

Alghamdi

et al. 2023

Smart Cities

View full text Add to dashboard Cite

The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.

show abstract

“…In regards to traditional recognition technologies, the most frequently used methods are smoke sensors, temperature sensors and gas sensors, which recognize the flame through its physical characteristics, such as solid particles, temperature and the release of CO and CO 2 [1][2][3]. However, these methods all require the sensors to keep a certain distance from the flame.…”

Section: Introductionmentioning

confidence: 99%

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

et al. 2018

View full text Add to dashboard Cite

Accurate combustion state recognition of flame images not only plays an important role in social security, but also contributes to increasing thermal efficiency and product quality. To improve the accuracy of feature extraction and achieve the combustion state recognition, a novel method based on radial Chebyshev moment invariants (RCMIs) and an improved firefly algorithm-wavelet support vector machine (IFA-WSVM) model is proposed. Firstly, the potential flame pixels and the potential flame contour are obtained in the pre-processing phase. Then, the rotation, translation and scaling (RTS) invariants of radial Chebyshev moments are derived. Combing the region and contour moments, the RCMIs of pre-processed and edge images are calculated to construct multi-feature vectors. To enhance the recognition performance, an IFA-WSVM model is built, where the IFA is applied to search the best parameters of WSVM. Then, the IFA-WSVM model is used to recognize the combustion state. Finally, the result for case studies show that the proposed method is superior to methods based on HMIs and ZMIs, achieving the highest rate of 99.07% in real time. The IFA algorithm also outperforms other benchmark algorithms. Even for the images transformed by RTS and small size of training sets, the proposed method continues to exhibit the best performance.

show abstract

System to detect fire under surveillanced area

Cited by 10 publications

References 21 publications

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

Contact Info

Product

Resources

About