An evaluation of region based object detection strategies within X-ray baggage security imagery

By Samet Akcay, Toby P. Breckon. 2017

84 citations recorded by the source corpus.

Here we explore the applicability of traditional sliding window based convolutional neural network (CNN) detection pipeline and region based object detection techniques such as Faster Region-based CNN (R-CNN) and Region-based Fully Convolutional Networks (R-FCN) on the problem of object detection in X-ray security imagery. Within this context, with limited dataset availability, we employ a transfer learning paradigm for network training tackling both single and multiple object detection problems over a number of R-CNN/R-FCN variants. The use of first-stage region proposal within the Faster RCNN and R-FCN provide superior results than traditional sliding window driven CNN (SWCNN) approach. With the use of Faster RCNN with VGG16, pretrained on the ImageNet dataset, we achieve 88.3 mAP for a six object class X-ray detection problem. The use of R-FCN with ResNet-101, yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image. Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Longyin Wen

In Computer Vision and Pattern Recognition

1. Kuan-Hung Shih
2. Ching-Te Chiu
3. Jiou-Ai Lin
4. Yen-Yu Bu
5. Libo Zhang
6. Boying Wang
7. Longyin Wen
8. Xianglong Liu

In Radiology, Nuclear Medicine and Imaging

1. Libo Zhang
2. Longyin Wen
3. Xianglong Liu
4. Yanjun Wu

Citations in the corpus

• Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark (2021, background): “To advance the developments of prohibited items detection in X-ray images, some recent attempts devote to construct security inspection benchmarks [25, 1, 2, 26, 36].”
• Real-Time Object Detection With Reduced Region Proposal Network via Multi-Feature Concatenation (2020, background): “References [10] and [11] show that object detection using deep learning can assist in identifying objects in X-ray images.”