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Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items.

Authors :
Ejiyi, Chukwuebuka Joseph
Qin, Zhen
Ukwuoma, Chiagoziem Chima
Nneji, Grace Ugochi
Monday, Happy Nkanta
Ejiyi, Makuachukwu Bennedith
Chikwendu, Ijeoma Amuche
Oluwasanmi, Ariyo
Source :
Network: Computation in Neural Systems. Sep2024, p1-28. 28p. 8 Illustrations.
Publication Year :
2024

Abstract

Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model – EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model’s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0954898X
Database :
Academic Search Index
Journal :
Network: Computation in Neural Systems
Publication Type :
Academic Journal
Accession number :
179661105
Full Text :
https://doi.org/10.1080/0954898x.2024.2398531