Classification and object detection for architectural pathology: Practical tests with training set

Abstract

Image classification and object detection techniques have been widely discussed and developed in recent years; they are the basis of various prosperous applications, for example, real-Time mapping. Promising as it is, the practical test in the cultural heritage field encountered multiple problems. In this paper, the authors attempt to share the research experimentations and the empirical knowledge focusing on the classification and detection of architectural pathology. The tests are built on elaborated training sets annotated with analysed and in-Advance defined categories. The trained models were examined from the perspective of evaluation sets, model explanation and unseen datasets. The outcomes indicated the mistakes and confusions behind things and stuff in the object detection efforts, to which cultural heritage and architectural field are closely related. The model also reveals specific visual patterns for recognition from thousands of instances in the training set. By digging into different aspects of model performance, the potential and limitations of these techniques in practical applications can be better understood.