Unsupervised data augmentation for object detection

Abstract

Data augmentation has always been an effective way to overcome overfitting issue when the dataset is small. There are already lots of augmentation operations such as horizontal flip, random crop or even Mixup. However, unlike image classification task, we cannot simply perform these operations for object detection task because of the lack of labeled bounding boxes information for corresponding generated images. To address this challenge, we propose a framework making use of Generative Adversarial Networks(GAN) to perform unsupervised data augmentation. To be specific, based on the recently supreme performance of YOLOv4, we propose a two-step pipeline that enables us to generate an image where the object lies in a certain position. In this way, we can accomplish the goal that generating an image with bounding box label.

References in corpus (10)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• mixup: Beyond Empirical Risk Minimization
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
• Focal Loss for Dense Object Detection
• EfficientDet: Scalable and Efficient Object Detection
• Rich feature hierarchies for accurate object detection and semantic segmentation
• Adversarial Feature Hallucination Networks for Few-Shot Learning
• RoIMix: Proposal-Fusion among Multiple Images for Underwater Object Detection
• A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection