Improved Regional Proposal Generation and Proposal Selection Method for Weakly Supervision Object detection

Abstract

In recent years, object detection has made great progress with the continuous development of deep neural network. At present, there are many different fully supervised object detection algorithms in the field of computer vision, which are basically saturated, while object detection in a weakly supervised manner is more challenging than strongly supervised object detection. Since nowadays mature object detection algorithms rely heavily on strongly labeled datasets, but strong labeled datasets are very expensive and require huge datasets to support in order to train a better object detection model, weakly supervised object detection has received more and more attention. In this paper, a new module can be embedded in the framework of weakly supervised object detection, three modules are introduced into the weakly supervised object detection framework, which is used to generate high-quality proposals and screen these proposals, and finally selecting more accurate proposal boxes that are beneficial for subsequent training, and demonstrate their effectiveness on the PASCAL VOC2007 and PASCAL VOC2012 datasets, in which this paper achieves a significant improvement over the existing classic weakly supervised object detection algorithms with significant improvements.