Vehicle logo recognition based on depth residual shrinkage network

Zhaobo Lin

doi:10.1117/12.2671450

28 April 2023 Vehicle logo recognition based on depth residual shrinkage network

Zhaobo Lin

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261035 (2023) https://doi.org/10.1117/12.2671450
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

So as to reduce the interference of noisy information and enhance the accuracy of car marker image recognition in real life, a car marker image recognition model based on deep residual shrinkage network is proposed. The model combines attention mechanisms and soft thresholding function on the basis of deep residual network, which is used to eliminate noise and redundant information in the data, thus reducing the interference of noise information and improving the accuracy of image recognition. The experiments are conducted by adding Gaussian noise and pretzel noise to the car logo images shared by HFUT-VL on Github to simulate the car logo images captured under realistic conditions, forming a dataset of 200 images for each car logo with a total of 16000 images, and then training the deep residual shrinkage network with deep residual network and SENet algorithm model on the data. Then, the deep residual shrinkage network is compared with the deep residual network and SENet algorithm model to train the data, and some of the car mark images are tested. The results of the experiment show that the method has better performance than other deep neural network methods.

Citation Download Citation

Zhaobo Lin "Vehicle logo recognition based on depth residual shrinkage network", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261035 (28 April 2023); https://doi.org/10.1117/12.2671450

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available