Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling
This study presents an enhanced Faster R-CNN framework that incorporates elliptical bounding boxes to significantly improve building detection in off-nadir imagery, effectively reducing severe geometric distortions caused by oblique sensor angles. Off-nadir imagery enhances architectural detail capt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1247 |
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| author | Sejung Jung Ahram Song Kirim Lee Won Hee Lee |
| author_facet | Sejung Jung Ahram Song Kirim Lee Won Hee Lee |
| author_sort | Sejung Jung |
| collection | DOAJ |
| description | This study presents an enhanced Faster R-CNN framework that incorporates elliptical bounding boxes to significantly improve building detection in off-nadir imagery, effectively reducing severe geometric distortions caused by oblique sensor angles. Off-nadir imagery enhances architectural detail capture and reduces occlusions, but conventional bounding boxes, such as axis-aligned and rotated bounding boxes, often fail to localize buildings distorted by extreme perspectives. We propose a hybrid method integrating elliptical bounding boxes for curved structures and rotated bounding boxes for tilted buildings, achieving more precise shape approximation. In addition, our model incorporates a squeeze-and-excitation mechanism to refine feature representation, suppress background noise, and enhance object boundary alignment, leading to superior detection accuracy. Experimental results on the BONAI dataset demonstrate that our approach achieves a detection rate of 91.96%, significantly outperforming axis-aligned bounding boxes (65.75%) and rotated bounding boxes (87.13%) in detecting irregular and distorted buildings. By providing a highly robust and adaptable detection strategy, our approach establishes a new standard for accurate and shape-aware building recognition in off-nadir imagery, significantly improving the detection of distorted, rotated, and irregular structures. |
| format | Article |
| id | doaj-art-2e1bda51e0154f9ca8297789ea4e9622 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2e1bda51e0154f9ca8297789ea4e96222025-08-20T03:08:54ZengMDPI AGRemote Sensing2072-42922025-04-01177124710.3390/rs17071247Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement HandlingSejung Jung0Ahram Song1Kirim Lee2Won Hee Lee3Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Republic of KoreaDepartment of Location-Based Information System, Kyungpook National University, Sangju 37224, Republic of KoreaResearch Institute of Artificial Intelligent Diagnosis Technology for Multi-Scale Organic and Inorganic Structure, Kyungpook National University, Sangju 37224, Republic of KoreaDepartment of Location-Based Information System, Kyungpook National University, Sangju 37224, Republic of KoreaThis study presents an enhanced Faster R-CNN framework that incorporates elliptical bounding boxes to significantly improve building detection in off-nadir imagery, effectively reducing severe geometric distortions caused by oblique sensor angles. Off-nadir imagery enhances architectural detail capture and reduces occlusions, but conventional bounding boxes, such as axis-aligned and rotated bounding boxes, often fail to localize buildings distorted by extreme perspectives. We propose a hybrid method integrating elliptical bounding boxes for curved structures and rotated bounding boxes for tilted buildings, achieving more precise shape approximation. In addition, our model incorporates a squeeze-and-excitation mechanism to refine feature representation, suppress background noise, and enhance object boundary alignment, leading to superior detection accuracy. Experimental results on the BONAI dataset demonstrate that our approach achieves a detection rate of 91.96%, significantly outperforming axis-aligned bounding boxes (65.75%) and rotated bounding boxes (87.13%) in detecting irregular and distorted buildings. By providing a highly robust and adaptable detection strategy, our approach establishes a new standard for accurate and shape-aware building recognition in off-nadir imagery, significantly improving the detection of distorted, rotated, and irregular structures.https://www.mdpi.com/2072-4292/17/7/1247off-nadir imagerybuilding detectionelliptical bounding boxesrotated bounding boxesaxis-aligned bounding boxesgeometric distortion |
| spellingShingle | Sejung Jung Ahram Song Kirim Lee Won Hee Lee Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling Remote Sensing off-nadir imagery building detection elliptical bounding boxes rotated bounding boxes axis-aligned bounding boxes geometric distortion |
| title | Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling |
| title_full | Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling |
| title_fullStr | Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling |
| title_full_unstemmed | Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling |
| title_short | Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling |
| title_sort | advanced building detection with faster r cnn using elliptical bounding boxes for displacement handling |
| topic | off-nadir imagery building detection elliptical bounding boxes rotated bounding boxes axis-aligned bounding boxes geometric distortion |
| url | https://www.mdpi.com/2072-4292/17/7/1247 |
| work_keys_str_mv | AT sejungjung advancedbuildingdetectionwithfasterrcnnusingellipticalboundingboxesfordisplacementhandling AT ahramsong advancedbuildingdetectionwithfasterrcnnusingellipticalboundingboxesfordisplacementhandling AT kirimlee advancedbuildingdetectionwithfasterrcnnusingellipticalboundingboxesfordisplacementhandling AT wonheelee advancedbuildingdetectionwithfasterrcnnusingellipticalboundingboxesfordisplacementhandling |