Abstract: Significant progress has been made towards building accurate automatic object detection systems for a variety of parcel security check applications using convolutional neural networks.However,the performance of these systems often degrades when they are applied to new data that differs from the training data,for example,due to variations in X-ray imaging.In this paper,we propose a context-based and transmittance adaptive domain alignment method to address the above performance degradation.Firstly,by using color information existed in X-ray images,we design an attention mechanism to process each color channel of an X-ray image separately to solve the problem of color differences among different X-ray machines.Next,we develop a feature alignment method to reduce the statistics difference among different X-ray images generated by various manufacturers.Finally,we propose to use a context vector as a regularization for the improvement of adversarial training to improve the precision.Theme-thod proposed in this paper solves the problem of the accuracy degradation of the object detection in the test domain,which is different from the training domain.

Key words: Object detection, X-ray images, Domain alignment, Transmittance-adptation, Convolutional neural network