Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items-Reference-Cited by-同舟云学术

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

Published:2024-09-11 Issue: Volume: Page:1-28
ISSN:0954-898X
Container-title:Network: Computation in Neural Systems
language:en
Short-container-title:Network: Computation in Neural Systems

Author:

Ejiyi Chukwuebuka Joseph¹²,Qin Zhen²,Ukwuoma Chiagoziem Chima³,Nneji Grace Ugochi³,Monday Happy Nkanta³,Ejiyi Makuachukwu Bennedith⁴,Chikwendu Ijeoma Amuche⁵,Oluwasanmi Ariyo²

Affiliation:

1. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China

2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. OBU Sino-British Collaborative Education, Chengdu University of Technology, Chengdu, Sichuan, China

4. Pharmacy Department University of Nigeria Nsukka, Enugu, Nigeria

5. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

the Sichuan Science and Technology Support Plan

the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/0954898X.2024.2398531

Reference49 articles.

1. GANomaly: Semi-supervised Anomaly Detection via Adversarial Training

2. Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery

3. Semantic Segmentation for Prohibited Items in Baggage Inspection

4. Multi-view object detection in dual-energy X-ray images

5. Object Recognition in Multi-View Dual Energy X-ray Images