Object Detection Methods for Autonomous Driving in Adverse Weather Conditions

Autonomous driving systems open up a new frontier in the automotive industry, offering new possibilities for future transportation with increased efficiency and a comfortable experience. However, object detection in adverse weather conditions remains a challenge that hinders the advancement of higher-level autonomous driving. This paper provides a detailed report on the latest developments in target detection algorithms under adverse weather conditions. It analyzes the impact of adverse weather on autonomous vehicle sensors, the establishment and use of related datasets, research on image restoration algorithms for adverse weather conditions and their effects on target detection tasks, and the application of domain adaptation methods in transfer learning for target detection. Additionally, the paper discusses future work and potential research areas in this field.