Scopus Indexed Publications

Paper Details


Title
Vision Anomaly Detection Using Self-Gated Rectified Linear Unit

Author
Israt Jahan,

Email

Abstract

In the area of image processing and computer vision, visual anomaly detection is a critical and difficult task. For anomaly detection in surface image data, a customized neural network incorporating self-gated rectified linear unit (SGReLU) was designed, and the SGReLU-based model excelled other activation function-based models with a top-20 average test accuracy of 99.84%. The computational time needed for the operation is 10533 s for 20 epochs and the top-20 average test loss is 0.0125 using SGReLU, both of them were comparatively less than other activation functions.


Keywords
Visualization , Computer vision , Computational modeling , Image processing , Neural networks , Data models , Task analysis

Journal or Conference Name
2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

Publication Year
2022

Indexing
scopus