Abstract
Threat detection in X-ray security images is critical for preserving public safety. Recently, deep learning algorithms have begun to be adopted for threat detection tasks in X-ray security images. However, most of the prior works in this field have largely focused on using image-level classification and object-level detection approaches. Adopting object separation as a pixel-level approach to analyze X-ray security images can significantly improve automatic threat detection. In this paper, we investigated the effects of incorporating segmentation deep learning models in the threat detection pipeline of a large-scale imbalanced X-ray dataset. We trained a Faster R-CNN (region-based convolutional neural network) model to localize possible threat regions in the X-ray security images on a balanced dataset to maximize detection of true positives. Then, we trained a DeepLabV3+ model to verify the preliminary detections by classifying each pixel in the threat regions, which resulted in the suppression of false positives. The two models were combined in one detection pipeline to produce the final detections. Experiment results demonstrate that the proposed method significantly outperformed previous baseline methods and end-to-end instance segmentation methods, achieving mean average precisions (mAPs) of 94.88%, 91.40%, and 89.42% across increasing scales of imbalance in the practical dataset.
Highlights
X-ray imaging is widely used for securing public spaces [1]
Both Mask R-convolutional neural network (CNN) and our proposed approach showed more robustness to the imbalance, proving that localized methods such as object-level and pixellevel approaches can drastically enhance the performance of threat detection models, which is of utmost importance in security applications
Our approach enjoys an even larger boost in performance compared to Mask R-CNN thanks to the uncoupling of the detection and segmentation task, which allowed for the use of the entire training set
Summary
X-ray imaging is widely used for securing public spaces [1]. Developing algorithms that provide aid to human inspectors to accomplish the monotonous and nontrivial task of detecting threats in X-ray security images is of utmost importance. Deep learning has become the most dominant method used in the field of automatic threat detection in X-ray security images [2]. The most prominent distinguishing feature of X-ray security images is the visibility of overlap between objects, which is a challenge when adopting deep learning models because object overlap aggravates intra-class variations [3]. The pixels in X-ray security images provide insight into the state of overlap between objects since each pixel corresponds to the radiation intensity that is attenuated by all overlapping objects [4]. Darker regions in X-ray security images signify higher attenuation, which could be caused by several overlapping objects or non-overlapping objects made up of higher density materials. Pixel-level deep learning method continues to be a popular approach used in the related field of medical X-ray imaging [5]. Pixel-level analysis can bring similar improvements to efficiency and reliability limited to X-ray security applications and encompassing other related X-ray imaging fields, such as structural materials inspection [6]
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