基于Focal Loss的多特征融合地物小目标检测

Multi Feature Fusion Small Target Detection Based on Focal Loss

针对无人机影像中地物车辆目标占整体像素不超过0.4%的小目标检测效果差的问题,在融合FPN结构的Faster R-CNN(FFRCNN)网络基础上,提出一种改进算法——FM-FFRCNN。利用Resnet-50网络进行特征提取,并联多个卷积核进行卷积操作实现多特征融合,达到扩大感受野的效果,并通过检测模块进行回归与分类。同时,为解决模型中正负样本不平衡问题,采用Focal Loss损失函数抑制背景样本对损失的贡献值。实验结果表明:FM-FFRCNN模型在平均精度(Average Precision,AP)上较原先模型提升了19.7%。

In order to solve the problem that the detection effect of small targets in UAV image is poor when the proportion of ground objects and vehicles in the whole pixel is less than 0.4%,an improved algorithm fm-ffrcnn is proposed based on the fast r-cnn(ffrcnn)network with FPN structure.Firstly,the resnet-50 network is used for feature extraction,and then multiple convolution kernels are connected in parallel to realize multi-feature fusion,so as to expand the receptive field.Finally,regression and classification are carried out by the detection module.At the same time,in order to solve the imbalance problem of positive and negative samples in the model,the focal loss function is used to suppress the contribution value of background samples to the loss.The experimental results show that the average precision(AP)of fm-ffrcnn model is improved by 19.7%higher than that of the original model.