In this paper, we present a new framework to detect and recognize entire lanes and symbolic marks on high resolution road images. The first part of the framework utilizes local threshold to overcome the limitations of fixed threshold determination in road marking segmentation. The second part of the framework handles false detections caused by nearby objects on the roads such as vehicles and buildings by re-moving the areas that are not related to road surface using semantic segmentation. It also boosts recognition performance with a cascaded classifier structure that combines CNN for symbolic mark recognition and SVM for lane verification. The proposed lane detection achieves average Fl-score of 0.96 and symbol recognition achieves average Fl-score of 0.91. The proposed method is expected to advance the vehicle industry; with a GPU device, the proposed method can easily be embedded in smart vehicles.
No takes yet. Share an insight, caveat, or question.
Lim et al. (2018) studied this question.