Target Detection Algorithm Incorporating Visual Expansion Mechanism and Path Syndication

Abstract

Because the lack of semantic information exchange between characteristic layers, the SSD (Single Shot multibox Detector) algorithm has insufficient detection performance. To address this problem, a detection algorithm called VPE-SSD (Visual Path Enhancement SSD) by incorporating a visual expansion mechanism and path syndication proposed in this paper. Firstly, a visual expansion mechanism is added to the shallow characteristic layer to increase the perceptual field. This enables the semantic information in the shallow layer to be more fully utilized by the network. It can also achieve the purpose of enhancing the expressiveness of the shallow feature layer. Then, the processed deep and shallow characteristic layers are fed into the path syndication module for bi-directional fusion. This improves the global information of the feature layers and generates multi-scale global feature maps. Next, to enhance the detailed information of deep characteristics and improve their expression, the deep characteristic enhancement module is applied to the last three characteristic maps. Finally, using the blended attention module to reduce the negative interference and improve the expression of characteristic maps during target detection. The experimental analysis of the VPE-SSD algorithm is conducted on VOC and COCO, and the mAP is 83.4% and 48.4%. From the result, VPE-SSD algorithm can make better use of the different size characteristic information which helps to improve the performance of the algorithm.