激光与光电子学进展, 2019, 56 (6): 061007, 网络出版: 2019-07-30
基于深度学习航拍图像检测的梯度聚类算法 下载: 1430次
Gradient Clustering Algorithm Based on Deep Learning Aerial Image Detection
图像处理 目标检测 深度学习 航拍图像 梯度聚类 区域建议方法 image processing object detection deep learning aerial image gradient clustering area proposal method
摘要
针对在目标检测中现有方法检测速度慢的问题,基于航拍图像中人造物体含有大量边缘的特点,提出了一种基于梯度聚类的区域建议算法(APM)。利用目标检测算法对提取的感兴趣区域进行检测,在DOTA (Dataset for Object deTection in Aerial Images)数据集上对算法的实时性和准确率进行了测试。研究结果表明,所提算法极大地提升了目标检测算法对大尺寸、目标密集的航拍图像的检测速度,该方法的召回率较高。
Abstract
An algorithm called gradient clustering based area proposal method (APM) is proposed to solve the problem that the existing methods are slow to detect objects, which is based on a large number of edges of artificial objects in aerial images. Then the extracted regions of interest are detected by the object detection method. The real-time performance and precision rate of this method are evaluated on the DOTA (Dataset for Object Detection in Aerial Images). The research results show that the proposed method greatly improves the detection speed of large-size, target-dense aerial images by the object detection algorithm, and still keeps a high recall rate.
解博, 朱斌, 张宏伟, 马旗, 张扬. 基于深度学习航拍图像检测的梯度聚类算法[J]. 激光与光电子学进展, 2019, 56(6): 061007. Bo Xie, Bin Zhu, Hongwei Zhang, Qi Ma, Yang Zhang. Gradient Clustering Algorithm Based on Deep Learning Aerial Image Detection[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061007.