Paper
13 June 2024 X-ray image segmentation and metal prohibited items detection in transportation safety
Ya-qin Tang, Xia-fen Zhang, Sha-sha Zhang
Author Affiliations +
Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318013 (2024) https://doi.org/10.1117/12.3034305
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China
Abstract
For detecting prohibited items inside packages, visual image doesn’t work well as items overlapped or occluded. Thus, Penetrable X-ray image was employed to solve the occlusion problem by coloring items in different material, yet the overlapping problem still remains unresolved. Therefore, this paper proposes an X-ray image segmentation and metal prohibited items detection following a coarse-to-fine scheme. Firstly, the overlapping metal objects are clipped according to the value in the H channel and R channel. Secondly, individual metal items are extracted using regional growing. Thirdly, metal prohibited items are detected by matching its shape with those predefined in Database. Our experiment show that the proposed method can detect 94.74% metal prohibited items inside packages.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ya-qin Tang, Xia-fen Zhang, and Sha-sha Zhang "X-ray image segmentation and metal prohibited items detection in transportation safety", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318013 (13 June 2024); https://doi.org/10.1117/12.3034305
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image segmentation

X-rays

X-ray imaging

Metals

RGB color model

Feature extraction

Histograms

Back to Top