Oulhissane Lynda, Merah Mostefa, Moldovanu Simona, Moraru Luminita
Laboratory of Signals and Systems (LSS), Faculty of Science and Technology, Abdelhamid Ibn Badis University of Mostaganem, 11 Route Nationale, Kharouba, 27000, Mostaganem, Algeria.
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunărea de Jos University of Galati, 2 Stiintei Str., 800146, Galati, Romania.
Sci Rep. 2023 Aug 31;13(1):14262. doi: 10.1038/s41598-023-41651-y.
Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures-as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications.
检测雷管是一项具有挑战性的任务，因为它们很容易被误分类为无害的有机物质，尤其是在高行李吞吐量的情况下。特别值得关注的是对用于雷管检测的自动化安全X射线分析的关注。复杂的安全场景需要越来越先进的计算机辅助视觉组合。我们提出了一系列广泛的实验，以评估卷积神经网络（CNN）模型在输入图像质量通过操作改变时检测雷管的能力。我们利用小波变换领域的最新进展和已建立的CNN架构，因为这两者都可用于目标检测。使用了各种图像处理方法，并进一步评估了检测性能。分析了原始X射线图像以及使用对比度受限自适应直方图均衡化（CLAHE）、基于小波变换的方法和混合CLAHE RGB - 小波方法处理后的图像。结果表明，大量操作，如：边缘增强、改变颜色信息或小波变换提供的不同频率分量，可用于区分几乎相似的特征。发现基于小波的CNN实现了更高的检测性能。总体而言，这种性能说明了在机场安全应用中联合使用处理方法和深度CNN的潜力。
