Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images
In object detection on remote sensing images or aerial images, high-resolution images and low relative area ratio of objects need to be solved. Usually, a high-resolution image should be split and detected separately. Then, the prediction results would be merged as a result of the complete image. Du...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10701299/ |
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| author | Lei Ge Lei Dou |
| author_facet | Lei Ge Lei Dou |
| author_sort | Lei Ge |
| collection | DOAJ |
| description | In object detection on remote sensing images or aerial images, high-resolution images and low relative area ratio of objects need to be solved. Usually, a high-resolution image should be split and detected separately. Then, the prediction results would be merged as a result of the complete image. Due to the overlap between adjacent image slices, non-maximum suppression (NMS) is used to suppress redundant prediction boxes during the merging process. However, redundant boxes from splitting are different from those generated by detection on a single image. Some special cases cannot be effectively solved by NMS. We have studied these cases and summarized judgment conditions to identify them. Based on NMS, we propose a new method named RODM-NMS for rotated object detection during merging slices of high-resolution images. Cases challenging for NMS are classified into two categories and addressed with specific handling methods. Compared to the traditional NMS, RODM-NMS could achieve a 3.9 improvement in AP50 during merging detection results on <inline-formula> <tex-math notation="LaTeX">$640\times 640$ </tex-math></inline-formula> slices of DOTA by FCOSR. It has low requirements for the overlap gap of slices and shows a significant improvement in performance for weak models. Hence, it is more suitable for computing-constrained mobile platforms such as drones to detect objects on high-resolution images. |
| format | Article |
| id | doaj-art-15ccdd285ffe4c94afa5292e55b19139 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-15ccdd285ffe4c94afa5292e55b191392025-08-20T01:47:50ZengIEEEIEEE Access2169-35362024-01-011214999915000710.1109/ACCESS.2024.347081510701299Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution ImagesLei Ge0https://orcid.org/0000-0002-5697-0087Lei Dou1https://orcid.org/0000-0002-8889-6862National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, ChinaNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, ChinaIn object detection on remote sensing images or aerial images, high-resolution images and low relative area ratio of objects need to be solved. Usually, a high-resolution image should be split and detected separately. Then, the prediction results would be merged as a result of the complete image. Due to the overlap between adjacent image slices, non-maximum suppression (NMS) is used to suppress redundant prediction boxes during the merging process. However, redundant boxes from splitting are different from those generated by detection on a single image. Some special cases cannot be effectively solved by NMS. We have studied these cases and summarized judgment conditions to identify them. Based on NMS, we propose a new method named RODM-NMS for rotated object detection during merging slices of high-resolution images. Cases challenging for NMS are classified into two categories and addressed with specific handling methods. Compared to the traditional NMS, RODM-NMS could achieve a 3.9 improvement in AP50 during merging detection results on <inline-formula> <tex-math notation="LaTeX">$640\times 640$ </tex-math></inline-formula> slices of DOTA by FCOSR. It has low requirements for the overlap gap of slices and shows a significant improvement in performance for weak models. Hence, it is more suitable for computing-constrained mobile platforms such as drones to detect objects on high-resolution images.https://ieeexplore.ieee.org/document/10701299/Non-maximum suppressionrotated object detectionremote sensingsmall object detection |
| spellingShingle | Lei Ge Lei Dou Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images IEEE Access Non-maximum suppression rotated object detection remote sensing small object detection |
| title | Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images |
| title_full | Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images |
| title_fullStr | Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images |
| title_full_unstemmed | Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images |
| title_short | Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images |
| title_sort | non maximum suppression for rotated object detection during merging slices of high resolution images |
| topic | Non-maximum suppression rotated object detection remote sensing small object detection |
| url | https://ieeexplore.ieee.org/document/10701299/ |
| work_keys_str_mv | AT leige nonmaximumsuppressionforrotatedobjectdetectionduringmergingslicesofhighresolutionimages AT leidou nonmaximumsuppressionforrotatedobjectdetectionduringmergingslicesofhighresolutionimages |