基于改进的更快R-CNN的目标高精度水平区域检测

Yu Liu, Dejun Li, Xiao Sun, Jian Chen, Zhaohong Xu
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引用次数: 0

摘要

针对遥感图像中目标的多尺度、多方位、变形等特点,本文提出了一种具有可变形卷积的Fast R-CNN算法,该算法可以显著提高舰船目标的检测精度,缩短处理时间。根据船舶宽高比大的特点,采用k均值聚类方法对锚架的大小和比例进行调整,使改进算法更适合于检测近似垂直或水平目标,并能获得高精度的船舶目标水平区域检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Target high precision horizontal region detection based on improved faster R-CNN
Aiming at the multi-scale, multi-directional and deformation of targets in remote sensing images, a Fast R-CNN algorithm with deformable convolution is proposed in this paper, which can significantly improve the detection accuracy of ship targets and shorten the processing time.According to the characteristics of large aspect ratio of ships, the size and ratio of anchor frame are adjusted by the K-means clustering method , so that the improved algorithm is more suitable for detecting approximately vertical or horizontal objects, and can obtain high-precision horizontal region detection of ship targets.
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