# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 2 Related Works  
### Automatic Prediction of Prohibited Items in X-ray Scanned Images  
Contrary to other types of images, X-ray security inspection images have the following characteristics:  
* Personal items are often placed casually, which leads to randomly stacked and overlapped objects. Consequently, prohibited items are often occluded by the background;
* In X-ray images, objects composed of the same material will be assigned similar colors, which leads to indistinguishability between prohibited items and background;
* X-ray security inspection image itself relates to the privacy of the inspected person.  
These characteristics pose an enormous challenge to many aspects, including detection methods and dataset construction. Due to this, there are relatively few related contributions, especially to the latter.  
Earlier work mainly relied on the bag of visual words (BoVW) model to extract handcrafted features [7; 8; 9], then adopted support vector machine [10] (SVM) as a classifier to recognize and detect prohibited item objects [11]. Additionally, there are some studies which used sparse representation methods [12].  
As deep learning mounts, large scale datasets [13] become crucial. For dataset construction of X-ray security inspection images, the GRIMA (from Grupo de Inteligencia de Maquina, the name of Machine Intelligence Group at the Department of Computer Science of the Pontificia Universidad Catolica de Chile) database of X-ray images (GDXray)proposed in [14] was one of the very few public datasets for a long time. The initial purpose of this dataset is to help people study the performance of hand-crafted features. Hence, relatively few negative samples and a relatively simple background make it inapplicable to meet current demands. More recently, there were also some research works based on deep learning methods for the detection of prohibited items in X-ray security images [15; 16; 17]. With the help of transfer learning method based on ImageNet [18] pretrained models and some prevalent object detection techniques such as region-based convolutional neural networks (RCNN) [19], \"You Only Look Once\" (YOLO) [20], etc. They performed satisfactorily in private datasets or the GDXray dataset. The work completed in [21] proposed a large-scale security inspection X-ray benchmark (SIXray), consisting of more than one million images. There are 8929 images with six classes of prohibited items manually annotated, and each of these images contains at least one of them. The distribution of positive/negative samples in this dataset is extremely imbalanced, and the background information is highly complex. It makes SIXray much more challenging and closer to practical applications. This work provides an authoritative verification benchmark for the related research about X-ray security inspection images. In recent years, many studies have been conducted with the adoption of this dataset [22; 23; 24; 25]. Specific to object occlusion in X-ray security inspection image, research [26] proposed a dataset named occluded prohibited items X-ray (OPIXray) image benchmark. This dataset contains 8885 high-quality X-ray images of five classes of prohibited items. It is worthy to note that, to better validate the performance of detectors, the test set is divided into three parts according to occlusion levels.