SSD Network Based on Improved Convolutional Attention Module and Residual Structure

Abstract

SSD(single shot multibox detector) is a single-order detection algorithm based on convolution neural network.Compared with the two-stage detection algorithm,it can not meet the requirements of many practical applications,especially in the small target detection task.In order to solve this problem,this paper proposes a feature extraction network Res-Am CNN based on improved residual structure and convolutional attention module.The feature extraction ability of the network is greatly improved,and the additive fusion with upsample (AFU) is introduced into the original SSD pyramid structure for feature fusion to enhance the representation ability of shallow features.The experimental results on PASCAL VOC data set show that compared with the original SSD network and mainstream detection network,the mean average precision (mAP) of Res-Am AFU SSD (SSD with Res-Am CNN and AFU) network on VOC test set is 69.1%,which is ahead of one stage network in accuracy,close to two stage network,and greatly ahead of two stage network in speed.The experimental results on a small target test set show that the mAP of Res-AmAFU SSD network is 67.2%,which is 9.4% higher than that of the original SSD,and the method is more flexible and does not need pre training.

Keywords

• ssd network|target detection|convolutional neural network|residual structure|attention mechanism