SFE-SSD: 浅层特征增强的SSD算法在小目标检测问题中的研究与应用

SSD (Single Shot Multibox Dectetor)算法由于具有高速且高精度的检测性能,是目前最好的目标检测算法之一.但由于提取检测框的特征层的特征信息不足, SSD算法在小目标检测任务中表现不佳.为了解决这个问题,目前大部分方法以严重牺牲检测速度为代价提升目标检测模型的精度. 本文提出了SFE-SSD (Shallow Feature Enhancement SSD)提升SSD模型在小目标检测任务中的性能.首先我们采用反卷积操作对SSD算法中检测框金字塔特征层的最浅特征层进行特征扩张.接着通过特征融合机制对扩张后的特征层进行特征增强操作.浅层特征增强策略与SSD 的原始框提取金字塔特征层是并行结构,一定程度上是可以减少检测速度的损失.实验结果显示,我们的方法在PASCAL VOC 2007数据库上精度达到了78.4\%mAP高于SSD算法1.2\%,检测速度达到了81帧/秒,并且在小目标检测任务中有着显著的提升.

SFE-SSD: Shallow Feature Enhancement SSD for Small Object Detection

SSD (Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed, but fails to detect very small size object which lacks enough resolution and enough feature information. In order to solve this problem, the majority of existing methods improve accuracy at the cost of a heavy loss of speed. In this paper, we propose SFE-SSD (Shallow Feature Enhancement SSD) to improve performance of SSD model on small object detection based on a novel and lightweight way of feature enhancement module. Firstly, we apply deconvolution on the shallowest feature map in SSD''s feature pyramid to enlarge the feature map size and recover more feature details. Then, we introduce semantic information to the enlarged feature map by multi-scale feature fusion. In addition, SFE-SSD is designed to a parallel network structure, which could reduce loss of speed in some degree. Experimental results show that our approach achieved 78.4$\%$mAP and is higher than baseline SSD by 1.2$\%$ on PASCAL VOC2007, especially with significant improvement on small object detection. The testing speed of SFE-SSD is 81 FPS at the cost of a little loss of speed.