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Abstract | ||
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We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration. We introduce a novel modulated rotation loss to alleviate the problem and a rotation sensitivity detection network (RSDet) which consists of an eight-param single-stage rotated object detector and the modulated rotation loss. Our proposed RSDet has several advantages: 1) it reformulates the rotated object detection problem as predicting the corners of objects while most previous methods employ a five-param-based regression method with different measurement units. 2) modulated rotation loss achieves consistent improvement on both five-param and eight-param rotated object detection methods by solving the discontinuity of loss. To further improve the accuracy of our method on objects smaller than 10 pixels, we introduce a novel RSDet++ which consists of a point-based anchor-free rotated object detector and a modulated rotation loss. Extensive experiments demonstrate the effectiveness of both RSDet and RSDet++, which achieve competitive results on rotated object detection in the challenging benchmarks DOTA-v1.0, DOTA-v1.5, and DOTA-v2.0. We hope the proposed method can provide a new perspective for designing algorithms to solve rotated object detection and pay more attention to tiny objects. The codes and models are available at:
<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yangxue0827/RotationDetection</uri>
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Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSVT.2022.3186070 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Rotated object detection,modulated loss,point-based,tiny objects | Journal | 32 |
Issue | ISSN | Citations |
11 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Qian | 1 | 0 | 0.34 |
| Xue Yang | 2 | 45 | 6.20 |
| S. Peng | 3 | 332 | 40.36 |
| Xiujuan Zhang | 4 | 0 | 0.34 |
| Junchi Yan | 5 | 891 | 83.36 |