Intelligent Computer Vision Systems in the Processing of Baggage and Hand Luggage X-ray Images

The task of automating the monitoring of items allowed and prohibited to be brought on board an airplane is considered. The own sample of 18,000 X-ray images was prepared in cooperation with the Ulyanovsk Civil Aviation Institute to solute this task. Classification, object detection and segmentation algorithms are investigated using modern deep learning technologies and our dataset. Modifications of convolutional neural networks as well as attention-based networks or transformer-based computer vision architectures are proposed. Special attention is given to methods for optimizing models to enable real-time performance. High Accuracy, mean Average Precision—mAP (84.5%) and Dice-Score (87.22%) quality metrics and frames per second—FPS (>20 FPS) performance metrics are obtained. Suggestions for the application of the obtained results and further research ways are formulated.