Deep Belief Network For Smoke Detection

Pundir, Arun Singh; Raman, Balasubramanian

doi:10.1007/s10694-017-0665-z

Cited by 24 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, a large number of questionnaires need to be issued for quantitative analysis to verify the model and further analyze the influencing factors and weights in the process of co-creation of value of crowdsourcing services. 31,32 In this way, we can better find strategies and methods to enhance the level of value cocreation, improve the performance of crowdsourcing, and provide corresponding suggestions and references for industry companies and relevant government departments. 33…”

Section: Discussionmentioning

confidence: 99%

RETRACTED: Research on the value co-creation of the crowdsourcing services under the sharing economy

Bai

2021

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

With the great success of crowdsourcing business models in the context of the sharing economy, how crowdsourcing services work has gained wide attention. Based on the value co-creation theory, taking ZBJ website of Chinese crowdsourcing service platform ranking first on the Alexa platform as an example, the paper constructs the value co-creation process model of crowdsourcing services under the sharing economy from a dynamic perspective through qualitative analysis of the cases of employers and service providers and interview data. It opens the black box of the value co-creation process. Its contribution is that it proposes the dynamic changing rule of three-dimensional interaction, integration and authorization of crowdsourcing service value co-creation, the change of the role of crowdsourcing main body, and the function mechanism of crowdsourcing service value co-creation on crowdsourcing performance. And the value co-creation mode of different stages is determined. The value co-creation mode is the dominant logic of “service + product” In the release task phase. The value co-creation mode is the value creation of user-led logic originally in the stage of contract submission. The value co-creation mode is the dominant logic of “product + service” in the evaluation and reward stage.

show abstract

Section: Discussionmentioning

confidence: 99%

RETRACTED: Research on the value co-creation of the crowdsourcing services under the sharing economy

Bai

2021

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

show abstract

“…Luo et al [ 40 ] combined convolutional neural networks with traditional foreground extraction methods for smoke detection, extracted suspected smoke regions based on motion and color information, and used a CNN to extract regional features for classification. Pundir and Raman [ 41 ] input texture features into deep belief texture learning to train the smoke recognition model. Zhang et al [ 42 ] solved the problem of insufficient sample data by inserting real smoke images in the forest background and adopted Faster R-CNN to detect wildland forest fire smoke.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight CNN Model Based on GhostNet

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The existing deep learning models have problems such as large weight parameters and slow inference speed of equipment. In practical applications such as fire detection, they often cannot be deployed on equipment with limited resources due to the huge amount of parameters and low efficiency. In response to this problem, this paper proposes a lightweight smoke detection model based on the convolutional attention mechanism module. The model is based on the YOLOv5 lightweight framework. The backbone network draws on the GhostNet design idea, replaces the CSP structure of the FPN and head layers with the GhostBottleNeck module, adds a convolutional attention mechanism module to the backbone network layer, and uses the CIoU loss function to improve the regression accuracy. Using YOLOv5s as the benchmark model, the parameter amount of the proposed lightweight neural network model is 2.75 M, and the floating-point calculation amount is 2.56 G, which is much lower than the parameter amount and calculation amount of the benchmark model. Tested on the public fire dataset, compared with the traditional deep learning algorithm, the model proposed in the paper has better detection performance and the detection speed is significantly better than the benchmark model. Tested under the unquantized simulator, the speed of the proposed model to detect a single picture is 60 ms, which can meet the requirements of real-time engineering applications.

show abstract

“…The system was based on an unmanned aerial vehicle (UAV) including AlexNet, modified and unmodified GoogLeNet and VGG13. A deep belief network (DBN) was used for smoke detection in Pundir and Raman (2017). The performance of the DBN-based method was compared with the performances of ANNs and autoencoders.…”

Section: Introductionmentioning

confidence: 99%

Forest fire detection in aerial vehicle videos using a deep ensemble neural network model

Basturk

2023

AEAT

View full text Add to dashboard Cite

Purpose The purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos. Design/methodology/approach Presented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models. Findings The presented fusing approach significantly improves the detection accuracy of fire incidents in the input data. Research limitations/implications The computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models. Practical implications The simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images. Social implications By this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high. Originality/value This study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.

show abstract

Deep Belief Network For Smoke Detection

Cited by 24 publications

References 20 publications

RETRACTED: Research on the value co-creation of the crowdsourcing services under the sharing economy

RETRACTED: Research on the value co-creation of the crowdsourcing services under the sharing economy

A Lightweight CNN Model Based on GhostNet

Forest fire detection in aerial vehicle videos using a deep ensemble neural network model

Contact Info

Product

Resources

About