Robust Perception Under Adverse Conditions for Autonomous Driving Based on Data Augmentation

Many existing advanced deep learning-based autonomous systems have recently been used for autonomous vehicles. In general, a deep learning-based visual perception system heavily relies on visual perception to recognize and localize dynamic interest objects (e.g., pedestrians and cars) and indicative traffic signs and lights to assist autonomous vehicles in maneuvering safely. However, the performance of existing object recognition algorithms could degrade significantly under some adverse and challenging scenarios including rainy, foggy, and rainy night conditions. The raindrops, light reflection, and low illumination pose a great challenge to robust object recognition. Thus, A robust and accurate autonomous driving system has attracted growing attention from the computer vision community. To achieve robust and accurate visual perception, we target to build effective and efficient augmentation and fusion techniques based on visual perception under various adverse conditions. The unpaired image-to-image (I2I) synthesis is integrated for visual perception enhancement and effective synthesis-based augmentation. Besides, we design a two-branch architecture to utilize the information from both the original image and the enhanced image synthesized by I2I. We comprehensively and hierarchically investigate the performance improvement and limitation of the proposed system based on visual recognition tasks and network backbones. An extensive experimental analysis of various adverse weather conditions is also included. The experimental results have demonstrated the proposed system could promote the ability of autonomous vehicles for robust and accurate perception under adverse weather conditions.