The detection of dangerous objects in X-ray images of baggage has become important, particularly due to rising crime rates1. The performance of screening devices is strongly influenced by the target visibility, image display technology, and the security officers’ knowledge. However, visual inspection of these images is highly challenging due to the low prevalence of targets, variability in target visibility (resulting in lack of precision in object shape), overlapping objects, poor contrast that obscures image details, and the potential for causing false alarms2,3. Furthermore, the constant and repetitive nature of the task i.e., the security officers constantly looking at screens and frequently encountering the same types of detected objects, can lead to attention fatigue and impaired judgment4.
The most dangerous prohibited articles in passenger baggage are so called improvised explosive devices. Detecting the detonator of a bomb can be a challenge even for well-trained security officers. To address these issues, numerous algorithms and techniques have been developed to improve the quality of 2D radiographic images5,6,7,8,9,10,11,12,13. The Bag-of-Visual-Words (BoVW) detection technique, which is based on natural language processing and information retrieval, employs a statistical process for object detection and classification6. This technique has been successfully applied for the detection of explosives. It was used together with various other methods, including supervised feature learning by autoencoders approach7, K-Nearest Neighbors, Logistic Regression8, and Decision Trees9. BoVW was also employed for the detection of guns, shuriken or razor blades. These techniques are based on dictionaries formed for each class and the detection consists of Scale Invariant Feature Transform (SIFT) feature descriptors of randomly cropped image patches10. The BoVW model correlated to the Speeded up Robust Features (SURF) descriptor and Support Vector Machine (SVM) classifier was used for firearm detection, achieving an optimal true positive rate of 99.07% at a false positive rate of 20%11. Both random forest and SVM algorithms were used for firearm detection and a statistical accuracy of 94% was reported12. Single, two and multiple X-Ray views and four classifiers (i.e., Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, Binary Robust Invariant Scalable Keypoints and SURF) were considered to assess the performance of classification. A better performance of classification has been highlighted when a combination of two and multiple X-Ray views was considered13.
In recent years, CNNs have gained significant popularity in the field of X-ray image analysis for baggage screening14,15,16,17,18,19,20. The augmentation technique, a feature enhancement module and a multi-scale fused region of interest method enabled the development of new CNNs with more accurate and robust detection capabilities. Those CNNs have a significantly improved performance when dealing with densely cluttered backgrounds during the X-ray baggage inspection14. Various techniques were employed to overcome different shortcomings of deep CNNs which were caused by a shortage of training images. Thus, the transfer learning paradigm15,16, region-based CNN (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures such as RetinaNet were used to provide object localization variants17,18 or to detect various items in the X-ray image of the baggage. In a similar fashion, the You Only Look Once (YOLO) architecture was used for X-ray images of baggage classification and for hazardous materials identification19. Moreover, an anchor-free CNN-based object detection method was proposed to address the problem of dangerous objects detection20.
Wavelet transforms are a popular tool in image denoising. They are mostly used in denoising operations without any prior knowledge of the noise model. Also, they are a useful tool for image enhancement. Apart from image denoising, which is a subjective process, image enhancement alters the image features to make it more appealing to the human eye21. Wavelet edges effects are noticeable in the processed images but wavelets are not currently used in X-ray security inspection. Implementation of the wavelet transforms for X-ray security inspection is still lacking even if they are extensively used in several contexts of chest X-ray images, due to their strong predictive capabilities. Various studies reported only the implementation of machine learning or deep learning radiomics models for predicting COVID-19 prognosis, based on edge detection or radiomic features extraction22,23,24. While the utilization of wavelet transforms with CNNs in medical image processing has been extensively studied, their application in other domains, such as aviation and transport security, has been relatively limited.
In the case of X-ray airport security data, large amounts of training data are not always available and collecting X-ray data with the special request of data annotation is very expensive. A study devoted to the performance of screeners’ assessment in detecting bomb detonators with 2D and 3D imaging was conducted in Ref.25. Despite the lower image quality in 3D imaging, this study found that the performance was almost similar to that of 2D imaging. In another approach, an Unsharp masking USM + CLAHE algorithm to process radiographic images for airport security was developed to effectively overcome the color distortion induced by the CLAHE image enhancement26. Generally, the majority of the previous studies have primarily focused on the detection of secondary explosive devices (such as C4, TNT, etc.) in X-ray images during baggage inspection. Nowadays, there are limited studies that specifically address the detection of detonators in the X-ray baggage inspection process. For instance, a Dual-Energy method was used to detect dangerous objects, including detonators, by differentiating between organic and inorganic materials27. The majority of existing scientific publications dealing with this topic focus on various algorithms devoted to enhancing the image quality and to improve the detection performance of detonators28, but few concentrate on wavelet-based detonators detection in X-ray images.
The aim of this paper is to introduce a new and efficient scheme for detecting detonators in X-ray baggage images by comparing different image manipulation methods and by evaluating their impact on the predictive capabilities of the classification models. To overcome the underutilization of wavelets in X-ray security inspections, we introduce wavelets as a manipulation method, which can be used to obtain images with higher resolution and more defined details. This allowed us to gain insights into the validity of the manipulation processes and how they relate to the performance of detonators detection. The experiments are conducted using the High Tech Detection Systems (HTDS) database. In the proposed approach we have chosen and built a well-established CNN architecture which had achieved excellent performance in object classification and detection29. We have conducted an extensive ablation study to establish an optimal configuration model with good performance across the dataset30,31. Thus, we have experimented with different images corrupted by Gaussian and salt-and-pepper noise, various altered hyper-parameters and different layer structures. The proposed CNN architectures perform two stages of analysis: (i) detonators detection within the raw X-ray image using the deep CNN-based classifier, the TensorFlow and Keras libraries and, (ii) the same CNN classifier framework is used when the input image set is pre-processed using the following methods: the CLAHE algorithm, which operates independently on the RGB images and furthermore, also on the individual color channels, the wavelet transforms with the HH and HL sub-bands, and, a combination of CLAHE and RGB-wavelet transform techniques. The outputs are analyzed in terms of accuracy, precision, recall, F1-score, and classification.
Our novel contributions compared to other state-of-the-art approaches can be summarized as follows:
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We proposed a multiscale approach by combining CLAHE, wavelet transforms, and RGB-wavelet transforms with CNNs to address the issue that different X-ray image quality factors can make the detection of the detonators a difficult task.
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We conduct experiments on manipulated images to find the proper technique able to achieve the highest detection performance.
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The custom CNN architecture proved compatible with various image manipulation techniques, being able to exploit the distinguishability between classes of baggage, with and without detonators inside.
The proposed methods of image manipulation emulate various technical specifications and assess the detection performance. Besides its practical relevance, a comparison of these manipulation methods is also of theoretical interest. The proposed study validates wavelets as a new framework for further studies in multivariate multiresolution analysis of X-ray screening of passenger baggage.
In our opinion, these experiments are at the proof-of concept level. We have tried to demonstrate that our idea could be turned into a reality. However, at this stage, only relatively limited datasets are available for sound training and testing operation.
