AdvNet: Multi-Task Fusion of Object Detection and Semantic Segmentation

Abstract

Environment perception is essential for autonomous driving vehicles (ADVs), and vision plays a vital role in environmental perception. Object detection and semantic segmentation as the basic computer vision tasks have become the significant technology in the ADVs’ perception module. With the development of deep learning, the results of both tasks had an obvious leap. However, these advances have been driven by a powerful baseline system, which brings strict computing resource requirements. When deploying these two tasks on the same platform, real-time performance usually gets worse. In this paper, a method was presented for end-to-end lane segmentation and obstacle detection in real-time performance. In this method, a multi-task network was designed by fusing segmentation network architecture and detection network architecture. With a specific training strategy and a modified open dataset, this method has an excellent performance in detecting lane lines and obstacles simultaneously. When comparing with separately running detection and segmentation modules at the same computing platform, the method presented in this paper achieves better real-time performance and lower hardware requirements.