## 1 Introduction  
X-ray technology has been widely used in security inspection processes, which are essential to ensure public safety [1, 2, 3]. In these processes, perspective images can be obtained by scanning luggage or other containers using intense X-ray penetration, and it is possible to determine whether there is contraband inside the container without unpacking the contents. This method improves the speed of security inspection dramatically and is widely used in various locations, e.g., airports, bus stations, and high-speed railway stations [4, 5]. X-ray security inspection image data have the following characteristics. (1) The irregularity of the shooting background, especially when the background is similar to the prohibited goods, will seriously interfere with object identification. (2) Objects in luggage are typically placed randomly, and overlapping occlusion increases recognition difficulty. (3) Different contraband come in diverse sizes, and the sizes of the same or similar types of contraband can also differ. Note that the image obtained by transmission scanning will be distorted, and the angle of placing the articles will change, which introduces significant difficulties in terms of effective object identification. Currently, most security inspection work relies heavily on human visual discrimination, and staff typically only have a few seconds to perform inspections. At peak operation times, staff can easily be affected by environmental noise, fatigue, and other factors, which lead to a high rate of missing prohibited goods and potential safety hazards. As deep learning technology has been used in the security detection field in recent years [6, 7], the use of a deep learning target recognition network can improve the automation of security inspection processes significantly and is of great significance to improve the inspection efficiency and recognition accuracy. X-ray security inspection contraband detection is performed to locate and classify contraband in the image. Generally, traditional machine learning target detection methods use sliding windows to locate target objects [8; 9], and classifiers are employed to identify the target objects. Unfortunately, the feature extraction ability of such methods is weak, it is difficult to capture detailed information about the target, and detection is poor for contraband objects that are blocked and placed at different angles. However, the network structure of the deep learning target detection algorithm is deep, and the location and classification accuracies can be improved by fusing different features between the network's layers. Currently, deep learning target detection algorithms can be divided into two-stage and one-stage detection algorithms. Two-stage algorithms are represented by the R-CNN method [10], which first selects several candidate regions using independent algorithms. Then, the features of the candidate regions are extracted through networks. Finally, this method locates and classifies feature vectors using support vector machines and regressors. (2) A one-stage algorithm transforms the target detection task into a regression problem and predicts the target using an end-to-end network. This technique is sufficiently fast to realize real-time detection. YOLO [11] and SSD [12] are representative examples of one-stage detection algorithms. Deep learning technology has recently been used in X-ray security inspection processes. [PERSON] et al. first introduced deep learning technology to baggage classification of X-ray images, verified that deep learning is more suitable than other methods for X-ray image classification tasks, and proposed an anomaly detection model that uses a conditional generation countermeasure network to train a large number of X-ray images without contraband so that when the contraband appears, it will be marked as an abnormal condition in the image. On this basis, anomaly detection is performed on images with contraband, and good results have been achieved in several studies [13; 14; 15; 16].