Contrabands Detection in X-ray Screening Images Using YOLO Model

Abstract

With the wide application of X-ray screening machines, the intelligent recognition of contrabands in the X-ray screening images has been paid more and more attention. Contrabands detection in X-ray screening images is a challenging problem in the field of security detection due to the random distribution of the items, which can cause the overlapping the target objects and the other objects. It is difficult to segment the X-ray security images into the different candidate regions which contain different objects by traditional image processing and recognition algorithm. In recent years, YOLO (You only look once, a realtime object detection system) Model was presented which provides a simple framework to predict bounding boxes and class probabilities directly from full images. In this paper, a YOLO based model is used to detect the contrabands in X-ray screening images. The experimental results show that the precision and the recall rate of contrabands detection under simple background are respectively higher than 98 percent and 94 percent. In complex environment, the precision remains above 95 percent, but the recall rate of some kinds of contrabands dropped down to about 70 percent.