Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

The widespread application of Internet of Things technology, new broadband mobile networks, cloudy computing, and other new information technologies on the Internet has promoted the development of “smart cities.” The essence of the “smart city” is to make full use of advanced information technology to realize the intelligent management and operation of the city, thereby creating a better life for the urban people and realizing the harmonious and sustainable development of the city. This article is aimed at studying the innovation of intelligent enterprise management models in the context of smart cities. This article first analyzes and briefly describes the theoretical knowledge of the “smart city” and the current domestic development, then proposes the technology of data warehouses, and finally uses the multiple linear regression formula for analysis. The experimental results of this paper show that in the context of smart cities, the intelligent management model of enterprises has played an important role in the development of enterprises, which is 51.7% faster than the traditional model, and the accuracy is as high as 92.3%.

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“…The "wisdom level" is improved within. So far, the construction of a "smart city" based on information infrastructure has kicked off [11,12].…”

Section: Innovative Research Methods Of Intelligentmentioning

confidence: 99%

Innovative Research on Intelligent Enterprise Management Mode under the Background of Smart City

Yan

Fang

2022

Wireless Communications and Mobile Computing

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“…This is to easy the localization of the plate in the next step. The image subtraction results are called the candidate images because the objects that remained from this step are areas that became a candidate location of a license plate [18,19]. Figure 11 and Figure 12 identifies morphological-open and image-subtraction.…”

Section: Morphological Open and Image Subtractionmentioning

confidence: 99%

Vehicle plate localization and extraction based on hough transform and bilinear operations

Uthaib

Croock²

2020

IJEECS

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In general, the extraction of the vehicle plate is a previous step of plate recognition, and it actively studied for several decades. Plate localization is used in various security and traffic applications. In this paper, the proposed method is efficient to localize a plate for the multinational countries. The proposed method consists of three levels. The first level is the preprocessing that contains several steps. The digital camera capture images have been taken about twenty meters from the car with zooming two to three meters. Images are resampled using the zooming technique (bilinear interpolation) that makes the dimension of image (1024 x 768) pixels. The resampled images are resized to (940x 730). These images are converted to grayscale (green channel), and the bilateral filter is applied for removing noise. The second level is plate detection that adopts morphological operations, image subtraction, and vertical edge detection (Sobel). At the last Connected component analysis and Hough transform are used. The third level is the Deskew plate that notifies the plate may skew so that Hough transform is used to detect the largest line segment. Then, the images are rotated using bilinear interpolation. About 860 images are tested for different countries (Iraq, Belarus, Armenia, Hungary), and the accuracy is 98.99 % for extraction of the plate and 100% for the Deskew plate. Thus, the proposed system shows high efficiency in achievement.

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“…Machine and deep learning are both defined in a layered structure in artificial intelligence. Significant applications [1]- [3] and methods are currently increasing the demand for artificial intelligence (AI) approaches. The use of AI approaches in the health field has shown promising results, such as the identification of malaria cells [4].…”

Section: Introductionmentioning

confidence: 99%

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Hatem

Altememe

Fadhel

2023

IJEECS

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Deep learning is currently playing an important role in image analysis and classification. Diseases in maize diminish productivity, which is a major cause of economic damages in the agricultural business throughout the world. Researchers have previously utilized hand-crafted characteristics to classify images and identify leaf illnesses in Maize plants. With the advancement of deep learning, researchers can now significantly enhance the accuracy of object classification and identification. Using the "Corn or Maize Leaf Disease Dataset" from the Kaggle website, four forms of maize leaf diseases were investigated: blight, common rust, gray leaf spot, and healthy. The pictures obtained from these corn leaf illnesses are categorized using four deep convolutional neural network (CNN) models that have been pre-trained (GoogleNet, AlexNet, ResNet50 and VGG16). Accuracy, precision, specificity, recall, F-score, and time are the six metrics used to assess the performance of any transfer learning (TL) model. MATLAB programming software is used to design and train the TL models. The accuracy of each item in the dataset has been checked. It has been determined that GoogleNet, AlexNet, VGG16, and ResNet50 each have an accuracy of 98.57%, 98.81%, 99.05%, and 99.36%, respectively.

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Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

Cited by 8 publications

References 23 publications

Innovative Research on Intelligent Enterprise Management Mode under the Background of Smart City

Innovative Research on Intelligent Enterprise Management Mode under the Background of Smart City

Vehicle plate localization and extraction based on hough transform and bilinear operations

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

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