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Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms / Yunhao Li in Transactions in GIS, vol 26 n° 6 (September 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 6 (September 2022) . - pp 2440 - 2454 Titre : Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms Type de document : Article/Communication Auteurs : Yunhao Li, Auteur ; Chunxiao Zhang, Auteur ; Chang Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2440 - 2454 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] image virtuelle [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] paysage urbain [Termes IGN] segmentation d'image [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Virtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people. Numéro de notice : A2022-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/tgis.12882 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1111/tgis.12882 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101698 [article]