On the use of synthetic images in deep learning for defect recognition in industrial infrastructures

Abstract

The use of synthetic images in deep learning for object detection applications is recognized as a key technological lever in reducing time and cost constraints associated with data-driven processes. In this work, the applicability of training an instance recognition algorithm on a synthetic database in an industrial context is assessed based on the detection of dents in pipes. Photo-realistic artificial images are procedurally generated using a rendering software and used for the training of the YOLOv5 object recognition algorithm. Its prediction effectiveness is assessed on a small test set in different configurations to identify improvement steps towards the reliable use of artificial data in computer-vision.