YOFOR:航空画像における微小物体検出のためのオブジェクト領域のみに焦点を当てる
Heng Hu1, Hao-Zhe Wang1, Si-Bao Chen1
1School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
まとめ
この研究では、オブジェクト領域に焦点を当てることで、複雑なリモートセンシング画像のオブジェクト検出を強化する適応型ネットワークであるYOFORを紹介します。オブジェクトの密な局在化とクラス分布のバランスを適応的に取ることで、精度が向上します。
背景:
- 深層学習はオブジェクト検出を進歩させましたが、高解像度のリモートセンシング画像には依然として課題があります。
- 複雑な背景、オブジェクトの不均一な分布、クラスの不均衡は、既存の検出器のパフォーマンスを妨げます。
結論:
- YOFORは、VisDrone、DOTA、AI-TODデータセット全体で大幅な有効性と適応性を示しました。
- 提案された方法は、困難なリモートセンシングシナリオにおけるオブジェクト検出のための堅牢なソリューションを提供します。
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