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.
|Title of host publication||2018 IEEE Intelligent Vehicles Symposium, IV 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2018 Oct 18|
|Event||2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China|
Duration: 2018 Sep 26 → 2018 Sep 30
|Name||IEEE Intelligent Vehicles Symposium, Proceedings|
|Other||2018 IEEE Intelligent Vehicles Symposium, IV 2018|
|Period||18/9/26 → 18/9/30|
Bibliographical noteFunding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00152, Development of Smart Car Vision Techniques based on Deep Learning for Pedestrian Safety) and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2016-0-00464) supervised by the IITP(Institute for Information & communications Technology Promotion).
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Automotive Engineering
- Modelling and Simulation