Saliency information and mosaic based data augmentation method for densely occluded object recognition

Data augmentation methods are crucial to improve the accuracy of densely occluded object recognition in the scene where the quantity and diversity of training images are insufficient. However, the current methods that use regional dropping and mixing strategies suffer from the problem of missing foreground objects and redundant background features, which can lead to densely occluded object recognition issues in classification or detection tasks. Herein, saliency information and mosaic based data augmentation method for densely occluded object recognition is proposed, which utilizes saliency information as prior knowledge to supervise the mosaic process of training images containing densely occluded objects. And the method uses fogging processing and class label mixing to construct new augmented images, in order to improve the accuracy of image classification and object recognition tasks by augmenting the quantity and diversity of training images. Extensive experiments on different classification datasets with various CNN architectures prove the effectiveness of our method.