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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10701299/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |