尺度敏感损失与特征融合的快速小目标检测方法

Fast Small Object Detection Method with Scale-Sensitivity Loss and Feature Fusion

现有通用深度学习目标检测方法对中、大目标有着较好的检测精度,而对小目标检测精度较低,主要原因在于小目标训练数据少以及下采样后特征图分辨率过低.针对上述问题,一方面,提出一种尺度敏感损失函数用于分类热图的训练,使小目标能够对模型更新产生更大的影响;另一方面,利用反卷积与可变形卷积提出一种自上而下的特征融合方法,获得高分辨率、强语义的特征图来检测目标.在上述两个方面的基础上,设计一种尺度敏感与特征融合的小目标检测方法.在PASCAL VOC数据集上,对提出方法进行了实验验证,实验结果表明:相比于现有目标检测方法,本文方法在保持较快检测速度的同时,提升了小目标检测的精度.

The existing general deep learning target detection methods have good detection accuracy for medium and large targets,but the detection accuracy for small targets is low,mainly due to few data for small target training and low res⁃olution of feature map after down sampling.To solve the above problems,on the one hand,a scale sensitive loss function is proposed for the training of classified heatmaps,so that small targets can have a greater impact on model updating;on the other hand,a top-down feature fusion method is proposed by using deconvolution and deformable convolution to obtain high-resolution and strong semantic feature map for target detection.On the basis of the above two aspects,a small target detection method based on scale sensitivity and feature fusion is designed.Experimental results on PASCAL VOC dataset show that compared with the existing target detection methods,the proposed method can maintain a faster detection speed and improve the accuracy of small object detection.