基于并行附加特征提取网络的SSD地面小目标检测模型

针对SSD原始附加特征提取网络（Original Additional Feature Extraction Network，OAFEN）中stride操作造成图像小目标信息丢失和串联结构产生的多尺度特征之间冗余度较大的问题，提出了一种计算量小、感受野大的深度可分离空洞卷积（Depthwise Separable Dilated Convolution，DSDC），并利用DSDC设计了一个包含三个独立子网络的并行附加特征提取网络（Parallel Additional Feature Extraction Network，PAFEN）.PAFEN上路用两个DSDC提取尺寸为19*19和3*3的特征图；中路用一个DSDC提取尺寸为10*10的特征图；下路用两个DSDC提取尺寸为5*5和1*1的特征图.实验结果表明，在SSD框架内，PAFEN在mAP和检测时间等方面均优于OAFEN，适用于地面小目标的检测任务.

SSD with Parallel Additional Feature Extraction Network for Ground Small Target Detection

Aiming at the problems of small target information loss caused by stride operation and large redundancy among multi-scale feature maps generated by serial structure in original additional feature extraction network (OAFEN) of SSD,a depthwise separable dilated convolution (DSDC) with small computation and large field of receptivity is proposed;then a parallel additional feature extraction network (PAFEN) with three independent subnetworks is designed by using five DSDCs.In upper subnetwork of PAFEN,two DSDCs are used to extract 19*19 and 3*3 feature maps.In intermediate subnetwork of PAFEN,one DSDC is used to extract 10*10 feature maps.In lower subnetwork of PAFEN,two DSDCs are used to extract 5*5 and 1*1 feature maps.The experimental results show that within the framework of SSD,PAFEN is superior to OAFEN in terms of mAP and detection time,and is suitable for ground small target detection tasks.