论文标题
选择具有弱识别功能的小物体识别的选择方法
Chosen methods of improving small object recognition with weak recognizable features
论文作者
论文摘要
许多对象检测模型在小型对象检测的几个有问题的方面努力，包括样本数量少，缺乏多样性和低特征表示。考虑到甘斯属于生成模型类，其最初的目标是学会模仿任何数据分布。使用适当的GAN模型将增强低精度数据，从而增加其数量和多样性。该解决方案可能会导致改进的对象检测结果。此外，将基于GAN的架构纳入深度学习模型可以提高小物体识别的准确性。在这项工作中，提出了基于GAN的基于GAN的方法，以改善VOC Pascal数据集上的小物体检测。将该方法与不同流行的增强策略（例如对象旋转，偏移等）进行比较。实验基于QuasterRCNN模型。
Many object detection models struggle with several problematic aspects of small object detection including the low number of samples, lack of diversity and low features representation. Taking into account that GANs belong to generative models class, their initial objective is to learn to mimic any data distribution. Using the proper GAN model would enable augmenting low precision data increasing their amount and diversity. This solution could potentially result in improved object detection results. Additionally, incorporating GAN-based architecture inside deep learning model can increase accuracy of small objects recognition. In this work the GAN-based method with augmentation is presented to improve small object detection on VOC Pascal dataset. The method is compared with different popular augmentation strategies like object rotations, shifts etc. The experiments are based on FasterRCNN model.
