Identifying threat objects using faster region-based convolutional neural networks (faster R-CNN)

Abstract

Automated detection of threat objects in a security X-ray image is vital to prevent unwanted incidents in busy places like airports, train stations, and malls. The manual method of threat object detection is time-consuming and tedious. Also, the person on duty can overlook the threat objects due to limited time in checking every person’s belongings. As a solution, this paper presents a faster region-based convolutional neural network (Faster R-CNN) object detector to automatically identify threat objects in an X-ray image using the IEDXray dataset. The dataset was composed of scanned X-ray images of improvised explosive device (IED) replicas without the main charge. This paper extensively evaluates the Faster R-CNN architecture in threat object detection to determine which configuration can be used to improve the detection performance. Our findings showed that the proposed method could identify three classes of threat objects in X-ray images. In addition, the mean average precision (mAP) of the threat object detector could be improved by increasing the input image's image resolution but sacrificing the detector's speed. The threat object detector achieved 77.59% mAP and recorded an inference time of 208.96 ms by resizing the input image to 900 × 1536 resolution. Results also showed that increasing the bounding box proposals did not significantly improve the detection performance. The mAP using 150 bounding box proposals only achieved 75.65% mAP, and increasing the bounding box proposal twice reduced the mAP to 72.22%.