Next Article in Journal
Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment
Previous Article in Journal
A Broad Dual-Band Implantable Antenna for RF Energy Harvesting and Data Transmitting
Previous Article in Special Issue
Lightweight Multi-Scale Asymmetric Attention Network for Image Super-Resolution
Article

Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme

School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Marc Desmulliez
Micromachines 2022, 13(4), 565; https://doi.org/10.3390/mi13040565
Received: 31 December 2021 / Revised: 24 February 2022 / Accepted: 28 March 2022 / Published: 31 March 2022
X-ray imaging machines are widely used in border control checkpoints or public transportation, for luggage scanning and inspection. Recent advances in deep learning enabled automatic object detection of X-ray imaging results to largely reduce labor costs. Compared to tasks on natural images, object detection for X-ray inspection are typically more challenging, due to the varied sizes and aspect ratios of X-ray images, random locations of the small target objects within the redundant background region, etc. In practice, we show that directly applying off-the-shelf deep learning-based detection algorithms for X-ray imagery can be highly time-consuming and ineffective. To this end, we propose a Task-Driven Cropping scheme, dubbed TDC, for improving the deep image detection algorithms towards efficient and effective luggage inspection via X-ray images. Instead of processing the whole X-ray images for object detection, we propose a two-stage strategy, which first adaptively crops X-ray images and only preserves the task-related regions, i.e., the luggage regions for security inspection. A task-specific deep feature extractor is used to rapidly identify the importance of each X-ray image pixel. Only the regions that are useful and related to the detection tasks are kept and passed to the follow-up deep detector. The varied-scale X-ray images are thus reduced to the same size and aspect ratio, which enables a more efficient deep detection pipeline. Besides, to benchmark the effectiveness of X-ray image detection algorithms, we propose a novel dataset for X-ray image detection, dubbed SIXray-D, based on the popular SIXray dataset. In SIXray-D, we provide the complete and more accurate annotations of both object classes and bounding boxes, which enables model training for supervised X-ray detection methods. Our results show that our proposed TDC algorithm can effectively boost popular detection algorithms, by achieving better detection mAPs or reducing the run time. View Full-Text
Keywords: X-ray imaging; objective detection; image cropping; deep learning; features extraction X-ray imaging; objective detection; image cropping; deep learning; features extraction
Show Figures

Figure 1

MDPI and ACS Style

Nguyen, H.D.; Cai, R.; Zhao, H.; Kot, A.C.; Wen, B. Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme. Micromachines 2022, 13, 565. https://doi.org/10.3390/mi13040565

AMA Style

Nguyen HD, Cai R, Zhao H, Kot AC, Wen B. Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme. Micromachines. 2022; 13(4):565. https://doi.org/10.3390/mi13040565

Chicago/Turabian Style

Nguyen, Hong D., Rizhao Cai, Heng Zhao, Alex C. Kot, and Bihan Wen. 2022. "Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme" Micromachines 13, no. 4: 565. https://doi.org/10.3390/mi13040565

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop