Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery

Abstract

We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts of data to facilitate a complex end-to-end feature extraction and classification process. Within the context of Xray security screening, limited availability of object of interest data examples can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be optimized explicitly as a later secondary process towards this application domain. To provide a consistent feature-space comparison between this approach and traditional feature space representations, we also train Support Vector Machine (SVM) classifier on CNN features. We empirically show that fine-tuned CNN features yield superior performance to conventional hand-crafted features on object classification tasks within this context. Overall we achieve 0.994 accuracy based on AlexNet features trained with Support Vector Machine (SVM) classifier. In addition to classification, we also explore the applicability of multiple CNN driven detection paradigms such as sliding window based CNN (SW-CNN), Faster RCNN (F-RCNN), Region-based Fully Convolutional Networks (R-FCN) and YOLOv2. We train numerous networks tackling both single and multiple detections over SW-CNN/F-RCNN/R-FCN/YOLOv2 variants. YOLOv2, Faster-RCNN, and R-FCN provide superior results to the more traditional SW-CNN approaches. With the use of YOLOv2, using input images of size 544×544, we achieve 0.885 mean average precision (mAP) for a six-class object detection problem. The same approach with an input of size 416×416 yields 0.974 mAP for the two-class firearm detection problem and requires approximately 100ms per image. Overall we illustrate the comparative performance of these techniques and show that object localization strategies cope well with cluttered X-ray security imagery where classification techniques fail.

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

“…Using CIFAR-10 we formulate a leave one class out anomaly detection problem. For the application context of X-ray baggage screening [33], the UBA and FFOB datasets from [13] are used to formulate an anomaly detection problem based on the concept of weapon threat items being an anomaly within the security screening process.…”

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

“…In recent years, 2D X-ray image based threat object recognition has been extensively studied [1,2,24], although even the use of multiple view X-ray suffers from the challenges of object recognition under varying orientation and inter-object occlusion. This can be addressed by 3D X-ray CT imaging which provides abundant information as a 3D volume comprised of successive, parallel X-ray image slices [14].…”

“…Automatic threat recognition (ATR) intends to enable baggage screening more efficient and effective with only limited human intervention. Attempts have been made in recent works to address the ATR problem in 2D X-ray images [3,1,23,6,2] and 3D CT images [11,40,9,22,28,27,26,7,20]. Most existing works, however, focus on the recognition of threat objects having specific shape-based appearances (e.g., firearms, bottles, knives, etc.…”

“…Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. Figure 1: Threat objects in 2D X-ray images [2]. Left: threat object signatures of specific appearance (e.g., firearms) are easy to identify.…”

Understanding unconventional preprocessors in deep convolutional neural networks for face identification

“…This is likened to the recognition ability of the human visual cortex [1] which processes parts of the face, individually, and are put together as a global feature to make sense of a person's identity. In [31], the output of the last layer, which comprises the high-level feature vectors of a pretrained CNN, showed to generalize well to a new target dataset than fine-tuning some layers of network. It is on this reasons that the high-level feature vectors of the inception-V3 model is found useful to this study's face search problem.…”