VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH

We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.

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A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network

Sustainability ◽

10.3390/su12052149 ◽

2020 ◽

Vol 12 (5) ◽

pp. 2149 ◽

Cited By ~ 2

Author(s):

Jing Huang ◽

Jinle Kang ◽

Huimin Wang ◽

Zhiqiang Wang ◽

Tian Qiu

Keyword(s):

Neural Network ◽

Social Networks ◽

Convolutional Neural Network ◽

Risk Mitigation ◽

Pythagorean Theorem ◽

Reference Object ◽

Surveillance Video ◽

Traffic Surveillance ◽

Novel Approach ◽

Video Systems

Quickly obtaining accurate waterlogging depth data is vital in urban flood events, especially for emergency response and risk mitigation. In this study, a novel approach to measure urban waterlogging depth was developed using images from social networks and traffic surveillance video systems. The Mask region-based convolutional neural network (Mask R-CNN) model was used to detect tires in waterlogging, which were considered to be reference objects. Then, waterlogging depth was calculated using the height differences method and Pythagorean theorem. The results show that tires detected from images can been used as an effective reference object to calculate waterlogging depth. The Pythagorean theorem method performs better on images from social networks, and the height differences method performs well both on the images from social networks and on traffic surveillance video systems. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

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A Neural Network Approach for Traffic Prediction and Routing with Missing Data Imputation for Intelligent Transportation System

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114573 ◽

2021 ◽

pp. 114573

Author(s):

Robin Kuok Cheong Chan ◽

Joanne Mun-Yee Lim ◽

Rajendran Parthiban

Keyword(s):

Neural Network ◽

Missing Data ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Transportation System ◽

Traffic Prediction ◽

Data Imputation ◽

Network Approach ◽

Neural Network Approach ◽

Missing Data Imputation

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Fruit Disease Detection Using Convolution Neural Network Approach

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2018.7.2.1871 ◽

2018 ◽

Vol 7 (2) ◽

pp. 62-65

Author(s):

Shivani . ◽

Sharanjit Singh

Keyword(s):

Neural Network ◽

Early Stage ◽

Texture Features ◽

Convolution Neural Network ◽

Image Feature ◽

Disease Detection ◽

Neural Network Approach ◽

Disease Information ◽

Fruit Disease ◽

Farming Industry

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

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