One Stage Detection Network with an Auxiliary Classifier for Real-Time Road Marks Detection

Abstract

We construct a robust road mark detector that achieves high accuracy with real-time processing performance ( 32 fps) under nVidia Titan-X GPU. We combine one stage deep learning detector with auxiliary CNN classifiers as a robust road marks detector. We found out that one stage detector not only detects multiple objects via single inference efficiently, but also remains a good accuracy in performance perspective. However, to make it better, we add an extra CNN classifier as the back part of the proposed architecture to reduce false positive and get better accuracy. The proposed detector can achieve 86.8% mAP in our in-house six-class road mark database.