Automatic and Robust Object Detection in X-Ray Baggage Inspection Using Deep Convolutional Neural Networks
| Autor: | Limin Luo, Yang Chen, Gouenou Coatrieux, Bangzhong Gu, Rongjun Ge |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject 020208 electrical & electronic engineering Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Convolutional neural network Object detection Control and Systems Engineering Region of interest Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Task analysis Effective method Computer vision Artificial intelligence Electrical and Electronic Engineering Function (engineering) business media_common |
| Zdroj: | IEEE Transactions on Industrial Electronics. 68:10248-10257 |
| ISSN: | 1557-9948 0278-0046 |
| DOI: | 10.1109/tie.2020.3026285 |
| Popis: | For the purpose of ensuring public security, automatic inspection of X-ray scanners has been deployed at the entry points of many public places to detect dangerous objects. However, current surveillance systems cannot function without human supervision and intervention. In this article, we propose an effective method using deep convolutional neural networks to detect objects during X-ray baggage inspection. As a first step, a large amount of training data is generated by a specific data augmentation technique. Second, a feature enhancement module is used to improve feature extraction capabilities. Then, in order to address the foreground–background imbalance in the region proposal network, focal loss is adopted. Third, the multiscale fused region of interest is utilized to obtain more robust proposals. Finally, soft nonmaximum suppression is adopted to alleviate overlaps in baggage detection. As compared with existing algorithms, the proposed method proves that it is more accurate and robust when dealing with densely cluttered backgrounds during X-ray baggage inspection. |
| Databáze: | OpenAIRE |
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