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Performance releaser with smart anchor learning for arbitrary-oriented object detection
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DOI：10.1049/cit2.12136.png)
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摘要
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Arbitrary-oriented object detection is widely used in aerial image applications because of its efficient object representation. However, the use of oriented bounding box aggravates the imbalance between positive and negative samples when using one-stage object detectors, which seriously decreases the detection accuracy. We believe that it is the anchor learning strategy (ALS) used by such detectors that needs to take the responsibility. In this study, three perspectives on ALS design were summarised and ALS-Performance Releaser with Smart Anchor Learning (PRSAL) was proposed. Performance Releaser with Smart Anchor Learning is a dynamic ALS that utilises anchor classification ability as an equivalent indicator to anchor box regression ability, this allows anchors with high detection potential to be filtered out in a more reasonable way. At the same time, PRSAL focuses more on anchor potential and it is able to automatically select a number of positive samples that far exceed that of other methods by activating anchors that previously had a low spatial overlap, thereby releasing the detection performance. We validate the PRSAL using three remote sensing datasets-HRSC2016, DOTA and UCAS-AOD as well as one scene text dataset-ICDAR 2013. The experimental results show that the proposed method gives substantially better results than existing models.
Keyword:
anchor learning strategy
deep learning
object detection
remote sensing
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7.3
论文数:
669
被引数:
2.4K
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