Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models
The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building sha...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/13/12/433 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846104398222065664 |
|---|---|
| author | Xiao Wang Haizhong Qian Limin Xie Xu Wang Bohao Li |
| author_facet | Xiao Wang Haizhong Qian Limin Xie Xu Wang Bohao Li |
| author_sort | Xiao Wang |
| collection | DOAJ |
| description | The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recognition. In response to the above issues, by introducing object detection technology in computer vision, this article proposes a building shape recognition and classification method based on the YOLO object detection model. Firstly, for different types of buildings, four levels of building training data samples are constructed, and YOLOv5, YOLOv8, YOLOv9, and YOLOv9 integrating attention modules are selected for training. The trained models are used to test the shape judgment of buildings in the dataset and verify the learning effectiveness of the models. The experimental results show that the YOLO model can accurately classify and locate the shape of buildings, and its recognition and detection effect have the ability to simulate advanced human visual cognition, which provides a new solution for the fuzzy shape recognition of buildings with complex outlines and local deformation. |
| format | Article |
| id | doaj-art-ed52b137be064d2d97a2e43b54851e01 |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-ed52b137be064d2d97a2e43b54851e012024-12-27T14:30:05ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-12-01131243310.3390/ijgi13120433Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection ModelsXiao Wang0Haizhong Qian1Limin Xie2Xu Wang3Bohao Li4Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaThe recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recognition. In response to the above issues, by introducing object detection technology in computer vision, this article proposes a building shape recognition and classification method based on the YOLO object detection model. Firstly, for different types of buildings, four levels of building training data samples are constructed, and YOLOv5, YOLOv8, YOLOv9, and YOLOv9 integrating attention modules are selected for training. The trained models are used to test the shape judgment of buildings in the dataset and verify the learning effectiveness of the models. The experimental results show that the YOLO model can accurately classify and locate the shape of buildings, and its recognition and detection effect have the ability to simulate advanced human visual cognition, which provides a new solution for the fuzzy shape recognition of buildings with complex outlines and local deformation.https://www.mdpi.com/2220-9964/13/12/433cartographic generalizationbuilding shape recognitionbuilding classificationobject detectionYOLO |
| spellingShingle | Xiao Wang Haizhong Qian Limin Xie Xu Wang Bohao Li Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models ISPRS International Journal of Geo-Information cartographic generalization building shape recognition building classification object detection YOLO |
| title | Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models |
| title_full | Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models |
| title_fullStr | Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models |
| title_full_unstemmed | Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models |
| title_short | Recognition and Classification of Typical Building Shapes Based on YOLO Object Detection Models |
| title_sort | recognition and classification of typical building shapes based on yolo object detection models |
| topic | cartographic generalization building shape recognition building classification object detection YOLO |
| url | https://www.mdpi.com/2220-9964/13/12/433 |
| work_keys_str_mv | AT xiaowang recognitionandclassificationoftypicalbuildingshapesbasedonyoloobjectdetectionmodels AT haizhongqian recognitionandclassificationoftypicalbuildingshapesbasedonyoloobjectdetectionmodels AT liminxie recognitionandclassificationoftypicalbuildingshapesbasedonyoloobjectdetectionmodels AT xuwang recognitionandclassificationoftypicalbuildingshapesbasedonyoloobjectdetectionmodels AT bohaoli recognitionandclassificationoftypicalbuildingshapesbasedonyoloobjectdetectionmodels |