FSNet: A Target Detection Algorithm Based on a Fusion Shared Network

Abstract

Currently, in a target detection framework, a feature pyramid is widely used to capture differences resulting from the change in size of a detected object. Examples include the one-stage detector, the Deconvolutional Single-Shot Detector (DSSD), the RefineDet and the two-stage detector DetNet. Although these target detection frameworks with characteristic pyramid structure have achieved good results, they are limited by the facts that 1) they only use one feature layer of multi-scale information for prediction, and 2) they do not fully incorporate multi-scale inclusion of feature information. Therefore, based on Single-shot detector (SSD) architecture, we propose a novel one-stage target detector framework-fusion shared network (FSNet) which makes full use of feature information between multiple scale feature maps to more effectively detect objects of different scales. First, we introduce a fusion attention mechanism to fuse features between feature layers of different scales. Secondly, we combine this with multi-head structure fusion multi-layer features extracted as new features by a backbone network. Finally, we feed multi-layer fusion features into shared prediction module (shared PM). In the module, a new class feature pyramid for target detection is formed wherein the feature map used for prediction is composed of multiple levels of feature layers. In order to verify the effectiveness of this new FSNet target detection algorithm, we used the 320X320 input for FSNet, obtaining 80.7% mean accuracy (mAP) and 15 frames per second (Frame Per Second, FPS) in the PASCAL VOC 2007 test.