SSD Object Detection Algorithm with Cross-layer Fusion and Receptive Field Amplification

Abstract

In view of the lack of information interaction between different layers of single shot multibox detector(SSD) and the limitation of the model's receptive field,an improved SSD object detection algorithm,named ESSD(enhanced SSD),is proposed to improve the accuracy of object detection.First of all,using the original multi-scale feature map in the SSD model and using the idea of feature pyramid networks(FPN),a cross-layer information interaction module is designed,which enhances the semantic information capabilities of different layers and reduces the information difference of different layers.Then,in order to improve the receptive field and multi-scale detection capabilities of the model,a receptive field amplification module is designed.Finally,the batch normalization layer is used to reduce the training time and improve the convergence speed of the model.In order to evaluate the effectiveness of ESSD,experiments are conducted on the PASCAL VOC2007 and PASCAL VOC2012 test sets.Experimental results show that on the PASCAL VOC2007 data set,its mAP is 82.1% and the detection speed is 15.7FPS.Compared with the original SSD512,its mAP increases by 2.3%;on the PASCAL VOC2012 test set,its mAP reaches 80.6%,which is also 2.1% higher than SSD512.Experiments have proved that the ESSD detector can still meet the real-time performance under the condition of high detection accuracy.

Keywords

• object detection|information fusion|receptive field|multi-scale|ssd