Arbitrarily Oriented Object Detection in Remote Sensing Images Based on Improved YOLOv4-CSP

Arbitrarily oriented object detection in remote sensing images is a challenging task. At present, most of the algorithms are dedicated to improving the detection accuracy, while ignoring the detection speed. In order to further improve the detection accuracy and provide a more efficient model for sc...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.1-15
Hauptverfasser:	Zheng, Ziyang, Zhang, Ting, Liu, Zhaoying, Liu, Bo, Li, Yujian, Sun, Changming
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Anchors Arbitrarily oriented object detection Cluster analysis Clustering Deep learning Detection Detectors Distance distance and angle-intersection over union distance and angle-intersection over union (DAIoU) Feature extraction Intersections k-means Methods Object detection Object recognition Remote sensing Task analysis Training Vector quantization YOLOv4-CSP
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