基于强化底层特征的无人机航拍图像小目标检测算法

针对无人机航拍图像小目标检测整体精度低、漏检误检的问题,提出了一种新的基于强化底层特征的多尺度小目标检测方法。该方法以Faster R-CNN-ResNet-50-FPN为基础模型,首先,设计提出了新的DetNet-59特征提取网络;其次,设计了扁平的Flat-FPN特征融合网络来提高强化底层特征;最后通过引入soft-NMS解决小目标重叠问题。所提出的算法在VOC2007和VisDrone2019数据集上进行仿真实验测试,在时间消耗提升不大于2%的情况下,mAP较基础模型提高了约11%,并且检测精度也优于现阶段的常用算法。实验结果表明,该算法在保证实时性的同时可以有效提高小目标检测精度。

Small object detection algorithm on UAV aerial images based on enhanced lower feature

In order to solve the problem of low accuracy and residual error in small object detection on UAV aerial images,this paper proposed a new kind of multi-scale small target detection method based on enhanced lower feature.Basing on Faster R-CNN ResNet-50-FPN model,the algorithm enhanced the lower feature by designing the structure of DetNet-59 feature extraction network and Flat-FPN feature fusion network,and applied soft-NMS to face the appearance of overlapping small objects.From simulation test on VOC2007 and VisDrone2019,the method is able to increase mAP by 11%compared to the base model when time consumption is no more than 2%,and it also performs better in terms of accuracy than current common algorithms.It was proved that the algorithm can effectively improve the detection accuracy of small targets while ensuring real-time performance.