基于多尺度特征融合的自适应无人机目标检测

Multi-Scale Feature Fusion Based Adaptive Object Detection for UAV

针对无人机(UAV)航拍图像中目标占比较小、拍摄角度和高度多变等问题,提出了一种基于多尺度特征融合的自适应无人机目标检测算法。利用深度可分离卷积结合残差学习的优点,建立了轻量化特征提取网络。构建多尺度自适应候选区域生成网络,将空间尺寸一致的特征图按照通道维度进行加权融合操作,增强了特征对目标的表达能力,并利用语义特征指导网络在多尺度特征图上自适应生成与真实目标更加匹配的目标候选框。仿真实验表明,该算法有效提升了无人机航拍目标检测精度,具有较好的鲁棒性。

In the aerial image of unmanned aerial vehicle(UAV),the target is usually small,and the shooting angle and height are variable.To address the problems,we proposed an adaptive drone object detection algorithm based on the multi-scale feature fusion.First,lightweight feature extraction network was established using the advantages of deep separable convolution and residual learning.Second,a multi-scale adaptive candidate region generation network was constructed,and feature maps with the same spatial size were weighted and merged based on the channel dimensions,which enhance the feature expression ability to objects.Based on these multi-scale featured maps,the use of semantic features to generate target candidate frames can be more matchable with real objects.Moreover,simulation experiments demonstrate that this algorithm can effectively improve the accuracy of UAV detection and have better robustness.