联营
计算机科学
比例(比率)
跳跃式监视
人工智能
旋转(数学)
计算机视觉
最小边界框
模式识别(心理学)
图像(数学)
量子力学
物理
作者
Yu Liu,Zhiqiang Wang,Fengjing Zhang,Jun Xie,Zhaohong Xu
摘要
In order to obtain the scale information of ship targets effectively, we proposed an improved Faster R-CNN algorithm which integrated multi-scale region proposal and pooling of ROI, visual attention mechanism and rotation region regression and suppression, and ship targets can be positioned by the rotate quadrangle bounding boxes to obtain the scale information of them. Our improved model is based on the standard Faster R-CNN and is maintained through end-to-end training.
科研通智能强力驱动
Strongly Powered by AbleSci AI