Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme
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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/160540 |
| _version_ | 1826124391162839040 |
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| author | Nguyen, Hong Duc Cai, Rizhao Zhao, Heng Kot, Alex Chichung Wen, Bihan |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Nguyen, Hong Duc Cai, Rizhao Zhao, Heng Kot, Alex Chichung Wen, Bihan |
| author_sort | Nguyen, Hong Duc |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:19:53Z |
| format | Journal Article |
| id | ntu-10356/160540 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:19:53Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1605402022-07-26T07:14:31Z Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme Nguyen, Hong Duc Cai, Rizhao Zhao, Heng Kot, Alex Chichung Wen, Bihan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering X-Ray Imaging Objective Detection 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. Published version 2022-07-26T07:14:30Z 2022-07-26T07:14:30Z 2022 Journal Article Nguyen, H. D., Cai, R., Zhao, H., Kot, A. C. & Wen, B. (2022). Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme. Micromachines, 13(4), 565-. https://dx.doi.org/10.3390/mi13040565 2072-666X https://hdl.handle.net/10356/160540 10.3390/mi13040565 35457869 2-s2.0-85128338219 4 13 565 en Micromachines © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering X-Ray Imaging Objective Detection Nguyen, Hong Duc Cai, Rizhao Zhao, Heng Kot, Alex Chichung Wen, Bihan Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme |
| title | Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme |
| title_full | Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme |
| title_fullStr | Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme |
| title_full_unstemmed | Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme |
| title_short | Towards more efficient security inspection via deep learning: a task-driven x-ray image cropping scheme |
| title_sort | towards more efficient security inspection via deep learning a task driven x ray image cropping scheme |
| topic | Engineering::Electrical and electronic engineering X-Ray Imaging Objective Detection |
| url | https://hdl.handle.net/10356/160540 |
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