A Fast Recognition Algorithm of Online Social Network Images Based on Deep Learning

Meng, Weili; Mao, Chuanzhong; Zhang, Jin; Wen, Jing; Wu, Dehua

doi:10.18280/ts.360613

Cited by 20 publications

(12 citation statements)

References 24 publications

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“…Traditionally, image recognition is implemented in three steps [6]: image preprocessing, feature extraction and classifier training. During image preprocessing, the useless information in the original image is filtered out, making the useful information more detectable; During feature extraction, the items that reflect the class of the original image are extracted manually, facilitating image classification; During classifier training, the classifier is trained by deep learning based on the extracted features, creating a robust classification model.…”

Section: Introductionmentioning

confidence: 99%

An Image Recognition Algorithm for Defect Detection of Underground Pipelines Based on Convolutional Neural Network

Yan¹,

Song²

2020

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The proliferation of underground pipelines is a defining feature of urbanization. Regular inspection and maintenance are necessary to reduce the economic loss caused by pipeline defects. This paper aims to detect pipeline defects in video images with the aid of computer vision. Firstly, the recursive neural network (RNN) was added to the classic convolutional neural network (CNN) to acquire various features from the images. Then, the Fisher criterion was weighted and improved, and introduced to the least square error cost function, enhancing the recognition rate of the improved CNN. Finally, the improved CNN algorithm was verified through contrastive experiments on actual underground pipeline images. The research results shed new light on the defect detection and maintenance of underground pipelines in urban areas.

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Section: Introductionmentioning

confidence: 99%

An Image Recognition Algorithm for Defect Detection of Underground Pipelines Based on Convolutional Neural Network

Yan¹,

Song²

2020

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“…Deep learning is an important field of machine learning [1,2]. Unlike traditional methods for image recognition, deep learning, which mimics the visual perception of humans, expresses the image features in an abstract form rather than actively establish the key features.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges

Zhu

Song

2020

Applied Sciences

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This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an accurate manner. Specifically, the number of fully connected layers was reduced by one, and the Softmax classifier was replaced with a Softmax classification layer with seven defect tags. The weight parameters of convolutional and pooling layers were shared in the pre-trained model, and the rectified linear unit (ReLU) function was taken as the activation function. The original images were collected by a road inspection vehicle driving across bridges on national and provincial highways in Jiangxi Province, China. The images on surface defects of cement concrete bridges were selected, and divided into a training set and a test set, and preprocessed through morphology-based weight adaptive denoising. To verify its performance, the improved VGG-16 was compared with traditional shallow neural networks (NNs) like the backpropagation neural network (BPNN), support vector machine (SVM), and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges. Judging by mean detection accuracy and top-5 accuracy, our model outperformed all the contrastive methods, and accurately differentiated between images with seven classes of defects such as normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. The results indicate that our model can effectively extract the multi-layer features from surface defect images, which highlights the edges and textures. The research findings shed important new light on the detection of surface defects and classification of defect images.

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“…In recent years, artificial intelligence (AI) and machine learning (ML) have made rapid progress [6,7]. anks to the constant updates of algorithms and sensing techniques, autonomous vehicles (AVs) are poised to make up an important part of road traffic.…”

Section: Introductionmentioning

confidence: 99%

Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata

Huang

Guo³

2020

Mathematical Problems in Engineering

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This paper attempts to disclose the features of the mixed traffic flow of manually driven vehicles (MVs) and autonomous vehicles (AVs). Considering dynamic headway, the mixed traffic flow was modelled based on the improved single-land cellular automata (CA) traffic flow model (DHD) proposed by Zhang Ningxi. The established CA model was adopted to obtain the maximum flow of the mixed traffic flow and was analyzed under different proportions of AVs. On this basis, the features of the mixed traffic flow were summarized. The main results are as follows: the proportion of AVs has a significant impact on the mixed traffic flow; when the proportion reached 0.6, the flow of the whole lane was twice that of the MV traffic flow. At a low density, the AV proportion has an obvious influence on mixed traffic flow. At a high density, the mixed traffic flow changed very little, as the AV proportion increased from 0 to 5. The reason is that the flow of the whole lane is constrained by the fact that MVs cannot move faster. However, when the AV proportion reached 0.8, the flow of the whole lane became three times that at the proportion of 0.6. At the speed of 126 km/h, the flow rate was 2.5 times the speed limit of 54 km/h. The findings lay a theoretical basis for the modelling of multilane mixed traffic flow.

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A Fast Recognition Algorithm of Online Social Network Images Based on Deep Learning

Cited by 20 publications

References 24 publications

An Image Recognition Algorithm for Defect Detection of Underground Pipelines Based on Convolutional Neural Network

An Image Recognition Algorithm for Defect Detection of Underground Pipelines Based on Convolutional Neural Network

An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges

Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata

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