关键词: 小样本数据集；自动驾驶；数据增强；GAN；扩散模型

Abstract: Training autonomous driving algorithms based on deep learning faces challenges such as a limited amount of data and uneven data distribution. Traditional methods achieve data augmentation through image flipping, cropping and color transformation. However, these struggle to address the issue of uneven data distribution. Some approaches propose conducting model training on small sample data through complex parameter tuning and transfer learning. Nevertheless, often requires a significant amount of time for hyperparameter adjustment and is prone to overfitting problems. In this study, a generative adversarial network (GAN) is trained on a small sample dataset, and the generator is utilized to generate new sample data. The generated samples maintain the same style as the original sample data while expanding the number of samples. Additionally, through prompt engineering and diffusion models, the distribution of samples is further optimized. The newly added sample data and the original sample data are integrated together using an automatic annotation method for training the object detection network. The performance is evaluated separately on object detection models based on different backbone networks. The experimental results demonstrate that a YOLO model is trained on a small sample dataset. In the practical task of vehicle object recognition, the detection success rate is increased to 93%.

Key words: small sample dataset; autonomous driving; data augmentation; GAN; diffusion model