Feature Fusion and Enhancement for Single Shot Multibox Detector

Abstract

Single Shot MultiBox Detector (SSD) is an effective method for multi-targets detection. However, due to the small scale and less information, small targets detection is a difficult assignment for SSD. In order to address this issue, we design an framework based on SSD, namely feature fusion and enhancement for SSD. First, considering that the feature information contained in different scale feature maps is different, especially the shallow feature maps which contains more detailed information and lacks semantic information, we propose a method of fusing different scale feature maps to introduce abundant object information. Secondly, although the feature maps has multiple channels, the importance of different channels’ feature to the detected object is different. We introduce the feature enhancement module for effective feature enhancement. Training on the PASCAL VOC2007 and VOC2012, the proposed method has an mAP of 79.07%, which is 1.4% higher than SSD, and an FPS of 39 at the input size of 300 x300.