首页|Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images
Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images
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NSTL
Elsevier
X-ray imagery security screening is an essential component of transportation and logistics. In recent years, some researchers have used computer vision algorithms to replace inefficient and tedious man-ual baggage inspection. However, X-ray images are complicated, and objects overlap with one another in a semi-transparent state, which underperforms the existing object detection frameworks. To solve the severe overlapping problem of X-ray images, we propose a foreground and background separation (FBS) X-ray prohibited item detection framework, which separates prohibited items from other items to ex-clude irrelevant information. First, we design a target foreground and use recursive training to adaptively approximate the real foreground. Thereafter, with the constraints of X-ray imaging characteristics, a de-coder is employed to separate the prohibited items from other irrelevant items to obtain the foreground and background (FB). Finally, we use the attention module to make the detection framework focus more on the foreground. Our method is evaluated on a synthetic dataset with FB ground truth and two pub-lic datasets with only bounding box annotations. Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art solutions. Furthermore, experiments are performed in the case where only a small number of images contain the FB ground truth. The results indicate that our method requires only a small number of FB ground truths to obtain a performance equivalent to that of all FB ground truths. (c) 2021 Elsevier Ltd. All rights reserved.
X-ray imageryObject detectionForeground and background separation(FBS)Recursive training
NSTL
Elsevier
