Deep Learning for Detecting Dangerous Objects in X-rays of Luggage
Nikita Andriyanov
Abstract
The investigation presented in this text is the study of object detection algorithms in the task of analyzing images of baggage and hand luggage. A modified version of the YOLOv5 convolutional neural network with additional rechecking based on the VGG-19 network is proposed. The modification is based on transfer learning from the available images. A comparison is made with other known algorithms. The article shows that the application of the proposed model made it possible to achieve the value of the mean average recall (mAR) at the level of 87% for dangerous objects of five classes.
Topics & Concepts
Computer scienceArtificial intelligenceConvolutional neural networkTask (project management)Object detectionTransfer of learningObject (grammar)Deep learningPattern recognition (psychology)RecallValue (mathematics)Computer visionMachine learningEngineeringLinguisticsPhilosophySystems engineeringEnvironmental Sustainability and Technology