APPLICATION OF YOLO AND U-NET MODELS FOR BUILDING MATERIAL IDENTIFICATION ON SEGMENTED IMAGES

Abstract

This paper is devoted to the analysis of existing convolutional neural networks and experimental verification of the YOLO and U-Net architectures for the identification and classification of building materials based on images of destroyed structures. The aim of the study is to determine the effectiveness of these models in the tasks of recognising materials suitable for reuse and recycling. This will help reduce construction waste and introduce a more environmentally friendly approach to resource management. The study examined several modern deep learning models for image processing, including Faster R-CNN, Mask R-CNN, FCN (Fully Convolutional Networks), and SegNet. However, the choice was made on the YOLO and U-Net architectures. YOLO is used for fast object identification in images, which allows for quick detection and classification of building materials, and U-Net is used for detailed image segmentation, providing accurate determination of the structure and composition of building materials. Each of these models has been adapted to the specific requirements of building materials analysis in the context of collapsed structures. Experimental results have shown that the use of these models allows achieving high accuracy of segmentation of images of destroyed buildings, which makes them promising for use in automated resource control systems.