WANG Ji-wu, LUO Hai-bao, YU Peng-fei, LI Chen-yang
（School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China）
Abstract： In order to solve the problem of small objects detection in unmanned aerial vehicle (UAV) aerial images with complex background, a general detection method for multi-scale small objects based on Faster region-based convolutional neural network (Faster R-CNN) is proposed. The bird’s nest on the high-voltage tower is taken as the research object. Firstly, we use the improved convolutional neural network ResNet101 to extract object features, and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions. Finally, a deconvolution operation is added to further enhance the selected feature map with higher resolution, and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network. The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.
Key words： Faster region-based convolutional neural network （Faster R-CNN）； ResNet101； unmanned aerial vehicle (UAV); small objects detection； bird’s nest
CLD number： TP183 doi： 10.3969/j.issn.1674-8042.2020.01.002
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基于Faster R-CNN的无人机航拍图像小目标检测
王纪武， 罗海保， 鱼鹏飞， 李晨阳
（北京交通大学 机械与电子控制工程学院， 北京 100044）
摘 要：为了解决复杂图像背景下无人机航拍图像小目标检测问题， 提出了一种基于Faster R-CNN的多尺度小目标检测方法。 以高压塔上的鸟巢为检测对象， 首先通过改进卷积神经网络ResNet101对目标进行特征提取， 然后采用多尺度滑动窗口方式在不同分辨率卷积特征图上获取目标初始建议区域， 最后在选取的分辨率较高的卷积特征图上增加一个反卷积操作进一步对特征图的分辨率进行提升， 并作为建议窗口的特征映射层传入目标检测子网络中。 通过对无人机实际航拍图像中鸟巢的检测结果表明， 所提出的算法可以实现对航拍图像中小目标的精确检测。
关键词： Faster R-CNN； ResNet101； 无人机； 小目标检测； 鸟巢
引用格式： WANG Ji-wu， LUO Hai-bao， Yu Peng-fei， et al. Small objects detection in UAV aerial images based on improved Faster R-CNN. Journal of Measurement Science and Instrumentation, 2020, 11（1）： 11-16. ［doi： 10.3969／j.issn.1674-8042.2020.01.002］
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