Toward Human-in-the-Loop Prohibited Item Detection in X-ray Baggage Images

X-ray baggage security screening is a demanding task for aviation and rail transit security; automatic prohibited item detection in X-ray baggage images can help reduce the work of inspectors. However, as many items are placed too close to each other in the baggages, it is difficult to fully trust the detection results of intelligent prohibited item detection algorithms. In this paper, a human-in-the-loop baggage inspection framework is proposed. The proposed framework utilizes the deep-learning-based algorithm for prohibited item detection to find suspicious items in X-ray baggage images, and select manual examination when the detection algorithm cannot determine whether the baggage is dangerous or safe. The advantages of proposed inspection process include: online to capture new sample images for training incrementally prohibited item detection model, and augmented prohibited item detection intelligence with human-computer collaboration. The preliminary experimental results show, human-in-the-loop process by combining cognitive capabilities of human inspector with the intelligent algorithms capabilities, can greatly improve the efficiency of in-baggage security screening.