Fast Detection of Vehicle Type and Position in Images Based on Deep Neural Network

Document Type : Original Article

Authors

1 PhD student, Faculty of Electrical Engineering, Shahr Majlesi Azad University, Isfahan, Iran

2 Associate Professor, Faculty of Electrical Engineering, Shahr Majlesi Branch, Islamic Azad University, Isfahan, Iran

Abstract

Today, large-scale vehicles are scattered in different parts of the city and therefore need to be controlled by programmed systems. Automatically finding vehicles in the images and categorizing them is complicated because vehicles come in so many different shapes, colors, and models, and their designs are so different. Therefore, different methods of image analysis have been proposed to solve this problem. But there are some challenges such as the multiplicity of images in a scene, the coherence of the image of the vehicle and the image background, the presence of noise in the images and the tolerance to changes in light. In recent years, the use of deep neural networks has been proposed as an effective tool in identification despite the diversity of environmental conditions and objects. But the challenge of using deep neural networks is their high computational load. In this paper, a new approach is used to identify the type of vehicles, which uses a combination of VGG neural network and the Yolo image separation and tracking algorithm. This method improves the challenges of the previous methods and also reduces the computational load. The images are taken from two databases, ImageNet and COCO, and these databases are used to train and test the neural network. The results show that the designed system solves many problems well, including the speed of vehicle detection and the problem of computational load. The detection accuracy has increased by 2 to 3% compared to previous systems and has reached about 98%. The advantages of this approach include high-quality image detection and the use of a YOLO algorithm with an acceptable speed in detecting the type of vehicle.

Keywords

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Electronic and Cyber Defense
Volume 10, Issue 2 - Serial Number 38
October 2022
Pages 117-127

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History

  • Receive Date: 22 August 2021
  • Revise Date: 20 November 2021
  • Accept Date: 09 August 2022
  • Publish Date: 23 September 2022

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