A Large-scale Simulation Dataset: Boost the Detection Accuracy for Special Weather Conditions

Dongfang Liu, Yiming Cui, Zhiwen Cao, Yingjie Victor Chen

Abstract: Object detection is a fundamental task for autonomous driving systems. One bottleneck hindering detection accuracy is a shortage of well-annotated image data. Virtual reality has provided a feasible low-cost way to facilitate computer vision related developments. In autonomous driving area, existing public datasets from real world generally have data biases and cannot represent a wide range of weather conditions, such as rainy or snowy roads. To address this challenge, we introduce a new large-scale simulation dataset which is generated by an automated pipeline from a high realism video game. Our dataset focuses on weather conditions, which can be adopted to train networks to effectively detect objects under such conditions. We use extensive experiments to evaluate our dataset by comparing it with public datasets. The experiment results show that networks trained with our dataset outperform the networks trained by other public datasets. Our work demonstrates the effectiveness of using simulation data to address real-world challenges in the practice of object detection.