Development of Artificial Intelligence System for Dangerous Object Recognition in X-ray Baggage Images
Jeong-nam Lee, Hyun‐chong Cho
Abstract
The importance of flight safety has been highlighted lately due to the increase of aviation industry. Baggage screening tasks are still difficult and the failures of dangerous object detection are frequent despite the improvement of screening equipment. The purpose of this study to develop the AI system on dangerous object detection for improving aviation safety. The convolutional neural network model, Xception, were applied to perform dangerous object recognition using X-ray baggage image dataset which contains 25,405 images of twelve items. Based on experiments, the accuracy and F1-score are 0.9939 and 0.9942. The significantly high success rate makes the model a very effective advisory or early warning tool, and an approach that could be further expanded to support a dangerous object identification system to operate in real airport screening process.