基于细化多尺度深度特征的目标检测网络

Object Detection Networks Based on Refined Multi-scale Depth Feature

现有深度卷积神经网络中感受野尺度单一,无法适应目标的尺度变化和边界形变,故此本文提出了一种提取并融合多尺度特征的目标检测网络.该网络通过减少池化并在网络底层加入空间加信道压缩激励模块来突出可利用的细节信息,生成高质量的特征图;此外,在深层网络中加入可变多尺度特征融合模块,该模块具有多种尺度的感受野并可根据物体边界预测采样位置,最后通过融合多尺度特征使网络具有更强的特征表达能力并且对不同尺度实例及其边界信息更具鲁棒性.实验证明,本文结构实现了比原有结构更高的平均精度,与目前主流目标检测算法相比也具有一定优势.

In the existing deep convolution neural network,the scale of receptive field is single,which could not adapt to the scale change and boundary deformation of the target.Therefore,a target detection network based on multi-scale feature extraction and feature fusion is proposed in this paper.The proposed network reduces pooling and adds space as well as channel compression excitation module at the bottom of the network to highlight the details and generate high-quality feature map.Besides,a variable multi-scale feature fusion module is added to the deep network,which has a multi-scale receptive field and can predict the position according to object boundary.Finally,the multi-scale feature fusion is used to enable the network of stronger ability of feature expression and is more robust to different scale and flexible boundary of instances.Experimental results show that the proposed structure achieves higher average accuracy than the original structure,and also has certain advantages compared with the state-of-the-art algorithms.