A high-precision forest fire smoke detection approach based on ARGNet

Zhan, Jialei; Hu, Yaowen; Zhou, Guoxiong; Wang, Yanfeng; Cai, Weiwei; Li, Liujun

doi:10.1016/j.compag.2022.106874

Cited by 58 publications

(26 citation statements)

References 26 publications

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“…Teachers' instructional impact is tested and judged through the corresponding teaching objectives and standards. Studying how to use CNN (Convolutional Neural Networks) [3][4][5] to assess the educational value of music art is therefore extremely important from both a theoretical and practical standpoint.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Lan

Fan²

2022

Journal of Environmental and Public Health

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In order to speed up the process of high-quality education and improve the level of education quality among the general public, people have pushed for the use of music art education in recent years. In this respect, this study covers the CNN-based assessment of the quality of music art teaching and creates a set of evaluation indices for that quality. The model architecture, network topology, learning parameters, and learning algorithm are all determined using this information, which also acts as the basis for the NN assessment model. The MATLAB simulation tool uses the CNN assessment model to train and learn a predetermined quantity of instructional quality data. The training experiment shows that this system can outperform other comparative systems in prediction accuracy by roughly 95%. Additionally, both the training and prediction accuracy of the model are completely acceptable. The evaluation findings and analytical data of the music art instructional quality assessment system created in this study can be used as a guide for determining the music art instructional quality and for making judgments regarding it.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Lan

Fan²

2022

Journal of Environmental and Public Health

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“…Rural public service, which is a crucial component of government work, is a specific nonexclusive and noncompetitive social service to meet the needs of farming, rural development, or farmers' production and daily lives. It is a form of rural public product that takes the form of labour services, agricultural information, or agricultural technology [ 3 ]. The growth of the rural economy, the sustainability of agriculture, and the raising of farmers' living conditions are all directly tied to accelerating the construction of the rural public service platform [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Monitoring and Path Selection of Rural Public Service Environment Based on Data Mining Technology in the New Era

Shu-min

2022

Journal of Environmental and Public Health

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The basic public service infrastructure in rural regions now has a lot of issues and flaws, which results in a low efficiency of basic public service delivery and substantially impedes the growth of the rural social economy and the enhancement of farmers’ quality of life. Building a smart platform for monitoring the rural public service environment, realising the growth of rural areas, creating a new socialist countryside holistically, and ensuring the sustainable, stable, and healthy development of rural society are all of great strategic importance under the new circumstances. This essay explores ways to further encourage the development of rural public service platforms. In order to choose the best course of action for rural public services in the new era, this study studies the practical problem of rural public services and, using DM (Data Mining) technology, mines the user characteristics of public service platforms. The experimental findings demonstrate that the algorithm’s accuracy is 94.38%. With the use of this technique, rural public services may efficiently mine user characteristics and offer specific technical support in the modern day. Decision-makers from all walks of life have an extraordinary desire for information concealed in huge amounts of data in the information age. The advantages of combining the two are becoming more widely recognised.

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“…Secondly, a lightweight feature-level and decision-level fusion module is proposed, which can not only improve the recognition ability of similar objects such as smoke and fog, but also ensure the real-time performance of the model. Zhan et al [36] proposed a recursive pyramid network with deconvolution and dilated convolution to solve the problem of low detection accuracy caused by high smoke transparency and unclear edges.…”

Section: Smoke Detectionmentioning

confidence: 99%

Multilevel feature cooperative alignment and fusion for unsupervised domain adaptation smoke detection

Zhou

Wen²,

Ma³

et al. 2023

Front. Phys.

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Early smoke detection using Digital Image Processing technology is an important research field, which has great applications in reducing fire hazards and protecting the ecological environment. Due to the complex changes of color, shape and size of smoke with time, it is challenging to accurately recognize smoke from a given image. In addition, limited by domain shift, the trained detector is difficult to adapt to the smoke in real scenes, resulting in a sharp drop in detection performance. In order to solve this problem, an unsupervised domain adaptive smoke detection algorithm rely on Multilevel feature Cooperative Alignment and Fusion (MCAF) was proposed in this paper. Firstly, the cooperative domain alignment is performed on the features of different scales obtained by the feature extraction network to reduce the domain difference and enhance the generalization ability of the model. Secondly, multilevel feature fusion modules were embedded at different depths of the network to enhance the representation ability of small targets. The proposed method is evaluated on multiple datasets, and the results show the effectiveness of the method.

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A high-precision forest fire smoke detection approach based on ARGNet

Cited by 58 publications

References 26 publications

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

[Retracted] Monitoring and Path Selection of Rural Public Service Environment Based on Data Mining Technology in the New Era

Multilevel feature cooperative alignment and fusion for unsupervised domain adaptation smoke detection

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