scholarly journals Vehicle Type Classification using Hybrid Features and a Deep Neural Network

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Classification Problems ◽  
Hybrid Features ◽  
Feature Vectors ◽  
Vehicle Type ◽  
Gabor Features ◽  
Toll Plaza ◽  
Vehicle Type Classification ◽  
Type Classification

Currently, considerable research has been done in vehicle type classification, especially due to the success of deep learning in many image classification problems. In this research, a system incorporating hybrid features is proposed to improve the performance of vehicle type classification. The feature vectors are extracted from the pre-processed images using Gabor features, a histogram of oriented gradients and a local optimal oriented pattern. The hybrid set of features contains complementary information that could help discriminate between the classes better, further, an ant colony optimizer is utilized to reduce the dimension of the extracted feature vectors. Finally, a deep neural network is used to classify the types of vehicles in the images. The proposed approach was tested on the MIO vision traffic camera dataset and another more challenging real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures.

scholarly journals Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System

2019 ◽  
Vol 13 (1) ◽  
pp. 63
Author(s):  
Wahyono Wahyono ◽  
Joko Hariyono
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification System ◽  
Learning Strategy ◽  
Classification Systems ◽  
Vehicle Classification ◽  
Vehicle Type ◽  
And Performance ◽  
Vehicle Type Classification ◽  
Type Classification

 Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.

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