Detection of Prohibited Objects in X-ray Images Based on YOLOv3
The detection of prohibited items in X-ray images based on human vision is often limited by work intensity and complex human flow environments,which brings huge challenges to security inspection work.The use of artificial intelli-gence methods for automatic detection and discrimination of prohibited items has important practical significance in assisting security inspection work.This paper proposes a target detection model for prohibited items in X-ray images based on YOLOv3,which adds a detection scale to the traditional YOLOv3 to achieve real-time automatic identification of prohibited items in X-ray images.This model not only identifies the types of prohibited items,but also calibrates the position of pro-hibited items in the image.The experimental results show that the improved YOLOv3 has achieved improvements in the e-valuation indicators of Precision,Recall,mAP,and F1 models,with a mAp value of 96.2%.It has good detection perfor-mance for prohibited object targets in X-ray images.
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