This paper is published in Volume-6, Issue-4, 2020
Area
Electronics Engineering
Author
Debasmita Mishra, Rashmita Routray
Org/Univ
College of Engineering and Technology, Bhubaneswar, Odisha, India
Pub. Date
06 August, 2020
Paper ID
V6I4-1329
Publisher
Keywords
Vehicle Detection, YOLO Architecture, R-CNN Architecture

Citationsacebook

IEEE
Debasmita Mishra, Rashmita Routray. Vehicle detection based on deep learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Debasmita Mishra, Rashmita Routray (2020). Vehicle detection based on deep learning. International Journal of Advance Research, Ideas and Innovations in Technology, 6(4) www.IJARIIT.com.

MLA
Debasmita Mishra, Rashmita Routray. "Vehicle detection based on deep learning." International Journal of Advance Research, Ideas and Innovations in Technology 6.4 (2020). www.IJARIIT.com.

Abstract

Vehicle detection is a challenging problem in autonomous driving systems, due to its large structural and appearance variations. In recent years convolutional neural networks (CNN) have achieved great success. Presently the state-of-art R-CNN and YOLO architectures are explored, implemented, and adopted for performance comparison on real-time data. From the experimental results, the R-CNN has faster detecting speed and accuracy in complex scenes. However, the transfer learning-based CNN model can be further analyzed and validated over other datasets and can be carried out. In comparison to the state-of-art, the proposed transfer learning approach may accurately regress the vehicle shape and classify vehicle fine-grained categories. Presently, the performances are validated over the UA-DETRAC datasets. The obtained performance strongly suggests the RCNN and has better classification accuracy. The proposed transfer learning approach can be a better alternative competing to the state-of-art approaches.