论文标题
在遥感图像中增强了用于小物体检测的单发探测器
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images
论文作者
论文摘要
小对象检测是一个具有挑战性的问题。在过去的几年中，卷积神经网络方法已经取得了长足的进步。但是，当前的探测器为小规模物体的有效特征提取而努力。为了应对这一挑战，我们提出了图像金字塔单杆检测器（IPSSD）。在IPSSD中，采用了单发探测器与图像金字塔网络相结合，以提取语义上强的特征来生成候选区域。提出的网络可以增强功能金字塔网络的小规模特征。我们评估了在两个公共数据集上提出的模型的性能，结果表明，与其他最新对象探测器相比，我们的模型的出色性能。
Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detectors struggle with effective features extraction for small-scale objects. To address this challenge, we propose image pyramid single-shot detector (IPSSD). In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions. The proposed network can enhance the small-scale features from a feature pyramid network. We evaluated the performance of the proposed model on two public datasets and the results show the superior performance of our model compared to the other state-of-the-art object detectors.
