Application research of image recognition technology based on CNN in image location of environmental monitoring UAV

Zhao, Kaixuan; He, Tingting; Wu, Shuang; Wang, Songling; Dai, Bilan; Yang, Qifan; Lei, Yiming

doi:10.1186/s13640-018-0391-6

Cited by 26 publications

(10 citation statements)

References 20 publications

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“…A convolutional neural network (CNN) is a class of deep learning algorithms that have been widely used for image processing (Zhao et al, 2018). In the present study, we applied this powerful algorithm for the analysis of the IFC data.…”

Section: Cnn-based Ifc Data Analysismentioning

confidence: 99%

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

Mochalova

Kotov

Lifanov

et al. 2021

Biotech & Bioengineering

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Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated phagocytosis has relied upon immunotherapy that has become of particular interest for the treatment of cancer and autoimmune diseases. A detailed investigation of phagocytosis is the key to the improvement of the therapeutic efficiency of existing medications and the creation of new ones. A promising method for studying the process is imaging flow cytometry (IFC) that acquires thousands of cell images per second in up to 12 optical channels and allows multiparametric fluorescent and morphological analysis of samples in the flow. However, conventional IFC data analysis approaches are based on a highly subjective manual choice of masks and other processing parameters that can lead to the loss of valuable information embedded in the original image. Here, we show the application of a Faster region-based convolutional neural network (CNN) for accurate quantitative analysis of phagocytosis using imaging flow cytometry data. Phagocytosis of erythrocytes by peritoneal macrophages was chosen as a model system. CNN performed automatic high-throughput processing of datasets and demonstrated impressive results in the identification and classification of macrophages and erythrocytes, despite the variety of shapes, sizes, intensities, and textures of cells in images. The developed procedure allows determining the number of phagocytosed cells, disregarding cases with a low probability of correct classification. We believe that CNN-based approaches will enable powerful in-depth investigation of a wide range of biological processes and will reveal the intricate nature of heterogeneous objects in images, leading to completely new capabilities in diagnostics and therapy.

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Section: Cnn-based Ifc Data Analysismentioning

confidence: 99%

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

Mochalova

Kotov

Lifanov

et al. 2021

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…For example, Ofli et al (2016) proposed a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Zhao et al (2018) selected the unmanned aerial vehicles remote sensing system as the platform to acquire image data, and studied the problem of image recognition based on residual neural network. Website application.…”

Section: Natural Disastermentioning

confidence: 99%

Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices

et al. 2020

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There has been an unsettling rise in the intensity and frequency of natural disasters due to climate change and anthropogenic activities. Artificial intelligence (AI) models have shown remarkable success and superiority to handle huge and nonlinear data owing to their higher accuracy and efficiency, making them perfect tools for disaster monitoring and management. Accordingly, natural disaster management (NDM) with the usage of AI models has received increasing attention in recent years, but there has been no systematic review so far. This paper presents a systematic review on how AI models are applied in different NDM stages based on 278 studies retrieved from Elsevier Science, Springer LINK and Web of Science. The review: (1) enables increased visibility into various disaster types in different NDM stages from the methodological and content perspective, (2) obtains many general results including the practicality and gaps of extant studies and (3) provides several recommendations to develop innovative AI models and improve the quality of modeling. Overall, a comprehensive assessment and evaluation for the reviewed studies are performed, which tracked all stages of NDM research with the applications of AI models.

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“…The Residual Neural Network (ResNet) is an innovative form of CNN to make the convolutional layer learn a residual rather than directly learn the target [10]. For example, Zhao et al (2018) [11]…”

Section: Introductionmentioning

confidence: 99%

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Han

2022

IEEE Access

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In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model's accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future INDEX TERMS deep learning; Internet of Things; signal acquisition; system design; vehicle identification and positioning

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Application research of image recognition technology based on CNN in image location of environmental monitoring UAV

Cited by 26 publications

References 20 publications

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

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