Object detection in aerial images is widely applied in many applications. In recent years, faster region convolutional neural network shows a great improvement on object detecting in natural images. Considering the size and distribution characteristic of object in remote sensing images, the region proposal network (RPN) should be changed before being adopted. In this letter, a scale adaptive proposal network (SAPNet) is proposed to improve the accuracy of multiobject detection in remote sensing images. The SAPNet consists of multilayer RPNs which are designed to generate multiscale object proposals, and a final detection subnetwork in which fusion feature layer has been applied for better multiobject detection. Comparative experimental results show that the proposed SAPNet significantly improves the accuracy of multiobject detection.

中文翻译：

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用于遥感图像中目标检测的缩放自适应建议网络

航拍图像中的目标检测被广泛应用于许多应用中。近年来，更快的区域卷积神经网络在自然图像中的目标检测方面取得了很大的进步。考虑到遥感图像中物体的大小和分布特征，区域建议网络（RPN）在采用之前应该改变。在这封信中，提出了一种尺度自适应提议网络（SAPNet）来提高遥感图像中多目标检测的准确性。SAPNet 由旨在生成多尺度目标提议的多层 RPN 和最终检测子网络组成，其中融合特征层已应用于更好的多目标检测。对比实验结果表明，所提出的 SAPNet 显着提高了多目标检测的准确性。