CLASSIFICATION AND OBJECT DETECTION FOR ARCHITECTURAL PATHOLOGY: PRACTICAL TESTS WITH TRAINING SET

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. I...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-2/W4-2024; pp. 477 - 484
Main Authors Zhang, K., Mea, C., Fiorillo, F., Fassi, F.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 14.02.2024
Copernicus Publications
Subjects
Classification
Cultural heritage
Cultural resources
Detection
Empirical analysis
Image classification
Object recognition
Pathology
Pattern recognition
Training
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Summary: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.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVIII-2-W4-2024-477-2024