Ganster R-CNN: Occluded Object Detection Network Based on Generative Adversarial Nets and Faster R-CNN

Abstract

Object detection has shown noticeably rapid improvement, despite most existing methods still scrabbling in occluded object detection. In response to this problem, this paper proposes a method for occluded object detection called Ganster R-CNN, which is on a basis of improved Generative Adversarial Nets (IGAN) and Faster R-CNN to enhance the detection ability of occluded objects. IGAN combines the generator of Generative Adversarial Nets and the detector of Faster R-CNN. By considering the lack of diversity of information in the feature maps, we first integrated feature maps from the shallow layer to the deep layer using Feature Pyramid Network. Next, the generator can generate occluded fake samples, and the scale of the training samples and the proportion of occluded objects in the dataset are expanded. Thus, the precision rate of occluded objects can be improved. Thus, the adversarial learning strategy can improve the detection ability of Faster R-CNN detector. Experiments show that compared with Faster R-CNN, this method achieves an improvement of +10.3 AP on the MS COCO dataset, and the mean average precision of this method is improved by 4.31% on the VOC2007 dataset and 3.92% on the VOC2012 dataset. Compared with classically existing models on PASCAL VOC datasets and some Transformer based models on MS COCO dataset, this method improves the average precision value and the mean average precision value of occluded objects.