# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 1 Introduction
Experiment results showed that pixel-level analysis significantly improved the threat detection performance, wherein the proposed method achieved a mean average precision of 94.88%, 91.40%, and 89.42%, across increasing imbalance ratios, outperforming previous baseline methods and an end-to-end instance segmentation method by a large margin. The rest of the paper is organized as follows: Section 2 briefly reviews the related works. Section 3 provides information on the imbalanced dataset. Section 4 defines the evaluation metrics. Section 5 discusses the methodology of the proposed approach. Section 6 describes the details of the experiments and  
other threat objects such as weapons. Since then, no work has attempted to solve the object separation task partially or completely. Alternatively, we briefly reviewed recent pixel-level approaches used in X-ray security images. The scarcity of works in this field is due to the difficulties in acquiring pixel-level annotation [2]. The study conducted in [13] evaluated classical machine learning techniques to segment objects in X-ray security images on the basis of color and later classify them into two broad classes, i.e., organic or inorganic. The work in [14] used a two-stage segmentation method for the broader task of intra-object anomaly detection. A closely related approach used semantic segmentation [15] to detect threat objects in X-ray images. However, semantic segmentation is not compatible with object separation since it cannot distinguish between instances. Apart from publishing SIXray, [PERSON] et al. initiated the research for threat detection in imbalanced X-ray security images by introducing the class-imbalance hierarchical refinement (CHR) approach [3], wherein a convolutional neural network (CNN), which approximates a function that removes weakly related elements from the feature map, is implemented in multiple levels of the convolution operation hierarchy. In our previous work [16], we explored the effect of filtering the initial image-level predictions using a generative adversarial network (GAN) [17] to learn the underlying distribution of negative samples so that we can designate the positives samples as anomalies when they deviate from the learned distribution. In this way, we can train a classifier on a balanced ideal dataset and suppress the induced false positives by identifying anomalies. Moreover, we also studied the effect of enlarging the pool of positive samples through image synthesis using different image-generating GANs [18]. We found that generating synthetic images by combining isolated threat objects and negative samples causes the model to disassociate the threat objects to the backgrounds distinct to positive samples, resulting in a better generalization and suppression of false positives for object-level threat detection. The deep feature fusion method in [19] combines early fusion by concatenating extracted features at the earlier stages of the classification model and late fusion by taking the weighted sum of losses calculated at different stages of the classification model. These components are integrated into a dual branch network with the aim of exploiting low-level spatial features and alleviating bias caused by the imbalanced data. A closely related approach is the tensor pooling method proposed in [20], where the authors train an instance segmentation network to classify pixels of threat objects using preprocessed inputs, wherein the image contours are extracted at different orientations to encode tensor representations. However, the authors did not consider the issue of overlap in X-ray security images and, hence, were not concerned with separating or isolating threat objects as a threat detection approach. Furthermore, their method requires that each variant of the threat category must be labeled, which exponentially increases the cost of annotation.