CODet: Component Object Detector Extracting Structural Features Based on Target Characteristics
Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023
This paper proposes CODet detection of remote sensing combined targets, with core contributions including: 1) cross level feature fusion module learning component structure and positional relationships; 2) The noise sparse sample allocation strategy alleviates the problems of classification localization misalignment and sample imbalance. Build a large-scale remote sensing image inference framework to accelerate inference by 3-4 times.
Recommended citation:
Z. Zhu, X. Sun, W. Diao, K. Chen, Q. He, G. Xu and K. Fu, "CODet: Component Object Detector Extracting Structural Features Based on Target Characteristics," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023, Art no. 5611914, doi: 10.1109/TGRS.2023.3281331.
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