Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy

Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy

In this paper, a vehicle type classification approach is proposed by using an enhanced feature extraction technique based on Sparse-Filtered Convolutional Neural Network with Layer-Skipping strategy (SF-CNNLS). To extract rich and discriminant vehicle features, we introduce Three-Channels of SF-CNNL...

Full description

Bibliographic Details
Main Authors: Suryanti, Awang, Nik Mohamad Aizuddin, Nik Azmi, Rahman, Md. Arafatur
Format: Article
Language:English
Published: IEEE 2020
Subjects:
T Technology (General)
Online Access:http://umpir.ump.edu.my/id/eprint/30744/
http://umpir.ump.edu.my/id/eprint/30744/8/Vehicle%20Type%20Classification%20Using%20an%20Enhanced%20Sparse-Filtered%20Convolutional%20Neural%20Network%20with%20Layer-Skipping%20Strategy.pdf

Internet

http://umpir.ump.edu.my/id/eprint/30744/
http://umpir.ump.edu.my/id/eprint/30744/8/Vehicle%20Type%20Classification%20Using%20an%20Enhanced%20Sparse-Filtered%20Convolutional%20Neural%20Network%20with%20Layer-Skipping%20Strategy.pdf

Similar Items