Prohibited Items Detection in X-ray Images in YOLO Network

Yajuan Wei, Chuan Dai, Minsi Chen, Zhijie Xu, Ying Liu, Jiulun Fan, Fang Ren, Zhao Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to safeguard public spaces from security issues, such as terrorism, security mechanisms have long played a crucial role. With the increase of population and crowd density in public transportation hubs of big cities, rapid, automatic, and accurate detection of prohibited items in X-ray scanning images becomes increasingly significant. Therefore, a one-stage detection algorithm, namely an improved You Only Look Once (YOLO) algorithm, is proposed. Firstly, the datasets are put to the the third version of YOLO(YOLOv3) network for iterative training by using a loss function named Distance Intersection over Union (DIoU). Secondly, the Spatial Pyramid Pooling (SPP)[15] model is utilized in the YOLOv3 network, can help to obtain feature maps from images of any size. Finally, the training and test results are visualized through the Tensorboard toolkit for performance evaluation. The experiment is also trained in two datasets named COCO and PASCAL VOC. The experimental findings demonstrate that the approach employed in this paper has better Frame Per Second (FPS) than other one-stage object algorithms such as Single Shot Multibox Detector (SSD), Resnet50-SSD and YOLOv3. The mean Average Precision (mAP) improves 2% than the original YOLOv3 network. The SIXRay datasets, derived from real images acquired of security checks in several subway stations, is used for testing under real-world conditions. Overall, the new method has been proven highly effective and holding promising potentials for large-scale implementation.

Original languageEnglish
Title of host publication2021 26th International Conference on Automation and Computing
Subtitle of host publicationSystem Intelligence through Automation and Computing, ICAC 2021
EditorsChenguang Yang
PublisherIEEE
Number of pages6
ISBN (Electronic)9781860435577
ISBN (Print)9781665443524
DOIs
Publication statusPublished - 15 Nov 2021
Event26th International Conference on Automation and Computing - University of Portsmouth, Portsmouth, United Kingdom
Duration: 2 Sep 20214 Sep 2021
Conference number: 26
http://www.cacsuk.co.uk/index.php/icac2021
https://www.ieee-ras.org/conferences-workshops/technically-co-sponsored/icac
https://ieeexplore.ieee.org/xpl/conhome/9594055/proceeding

Conference

Conference26th International Conference on Automation and Computing
Abbreviated titleICAC 2021
Country/TerritoryUnited Kingdom
CityPortsmouth
Period2/09/214/09/21
Internet address

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