DEVELOPMENT OF A MULTI-CLASS MODEL FOR CLASSIFICATION OF LAND, AIR AND WATER TRANSPORT BASED ON THE RESNET34 ARCHITECTURE (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2024)
This paper focuses on the identification and classification of land, air and water vehicles. The study used a deep learning approach to identify the type of vehicles based on images. In particular, a multi-class vehicle detection model was developed using a deep convolutional neural network based on the ResNet-34 architecture on a specially prepared dataset. The use of these technologies is becoming important not only for tracking and monitoring moving objects, but also for reducing traffic congestion, preventing traffic accidents and improving the reliability of unmanned vehicles.
Keywords: object detection; vehicle detection; multi-class classification; CNN; air; land; water