A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray營mage

X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence (AI) training, data samples are increased by using the CP (copy-paste) algorithm on the existing data. It also solves the data labeling problem by mixing supervised and semi-supervised learning. The four comparative models to be used in this study are Faster Region-based Convolutional Neural Networks Residual2 Network-101 (Faster R-CNN_Res2Net-101) supervised learning, Cascade R-CNN_Res2Net-101_supervised learning, Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2 (CBNetV2) Network-101 (Cascade R-CNN_ CBNetV2Net-101)_supervised learning, and Cascade R-CNN_ CBNetV2-101_semi-supervised learning which are then compared and evaluated. As a result of comparing the performance of the four models in this paper, in case of Cascade R-CNN_CBNetV2-101_ semi-supervised learning, Average Precision (AP) (Intersection over Union (IoU) = 0.5): 0.7%, AP (IoU = 0.75): 1.0% than supervised learning, Recall: 0.8% higher.