## 1 Introduction
In addition, [PERSON] et al. [17] employed a two-stage detection method with a double CNN structure to find the region of interest in the image, to obtain the corresponding location information, and then to classify the anomaly. Here, the detection accuracy was improved slightly by combining target detection and anomaly detection techniques. However, the network structure of this method was complex, detection speed was slow, and it was difficult to distinguish objects with similar shapes. Contraband hidden in suitcases are generally small, e.g., guns and knives, and detecting small dangerous objects is challenging. In addition, an X-ray image differs from a color image, and the details and color information of this kind of data are lost. Thus, most deep learning network models do not recognize overlapping objects effectively. Thus, [PERSON] et al. [18] proposed a front and back background segmentation method to remove most of the background containing useless information according to the brightness differences between the front and back scenes of the X-ray image. [PERSON] et al. [19] used the contour information of the object in the X-ray image, generated a series of tensors using cascade structure tensor technology, provided target suggestions, and then conducted target detection using a convolution neural network based on the contour information and candidate targets. Note that implementing an attention module can also effectively solve the occlusion problem. [PERSON] et al. [20] combined a semantic segmentation network with the mask R-CNN, where they took the semantic segmentation result as the soft attention mask of the mask R-CNN, which improved the detection accuracy of overlapping objects. In addition, [PERSON] et al. [21] proposed the XMC R-CNN model. They used the X-ray material classifier and organic and inorganic stripping algorithm for the first time to solve the problem of overlapping contraband in X-ray images. Although many previous studies have investigated the occlusion and overlap of targets in X-ray images, recognition accuracy and computation speed still limit the application of such technology in the security detection field. In summary, further research is required to develop deep learning networks that exhibit high recognition accuracy, good stability, and fast detection speeds. Given that traditional machine learning algorithms cannot handle the increasingly complex security environment. Based on an existing deep learning target detection network under the YOLO framework, we propose an innovative YOLO-T network structure to detect dangerous small-scale goods under complex environmental conditions, e.g., multiple targets and occlusion. The proposed YOLO-T model provides a complete method for target identification of dangerous goods based on X-ray images in the security inspection field. Through our experiments, the detection accuracy of the YOLO-T network reached 97.73%, and the best results were obtained in the comparison experiments with various networks in YOLO series. In addition, the proposed model solves the problems of difficulty and low precision when detecting small target objects in the security inspection field and improves the detection efficiency. From the perspective of practical application, public transport and airports are equipped with X-ray security detectors. The use of the depth learning model can quickly and accurately identify dangerous goods from the images taken by the X-ray security detector. When the flow of people is large and the safety detection task is heavy, rapid and automatic detection of dangerous goods is of great significance. This article's structure is as follows. The first part introduces the research status of X-ray image security detection. The second part introduces the basic principle of the YOLO-T method. In the third part, a comparative experiment is carried out between the YOLO-T method and other methods. The last part is the summary and prospect of the full research.