Abstract: Detecting prohibited items in X-ray security imagery is pivotal in maintaining border and transport security against a wide range of threat profiles. Convolutional Neural Networks (CNN) with the support of a significant volume of data have brought advancement in such automated prohibited object detection and classification. However, collating such large volumes of X-ray security imagery remains a significant challenge. This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery. Here we investigate the difference in detection performance achieved using real and synthetic X-ray train- ing imagery for CNN architecture detecting three exemplar prohibited items, Firearm, Firearm Parts, Knives, within cluttered and complex X-ray security baggage imagery. We achieve 0.88 of mean average precision (mAP) with a Faster R-CNN and ResNet101 CNN architecture for this 3-class object detection using real X-ray imagery. While the performance is comparable with synthetically composited X-ray imagery (0.78 mAP), our extended evaluation demonstrates both challenge and promise of using synthetically composed images to diversify the X-ray security training imagery for automated detec- tion algorithm training.
|Comments:||Presented at BMVC 2019: ODRSS 2019 Workshop on Object Detection and Recognition for Security Screening, Cardiff, UK.|
|Cite as:||Paper (PDF): ODRSS2019_1_1_Bhowmi.pdf|