Abstract
In this paper, we address the task of semantic image segmentation for road scene understanding. Road scene understanding is an important task in the field of computer vision. The main challenge is to develop an ability to recognize whole or partial objects in the road scene for various purposes such as surveillance systems, driving assistance systems, and autonomous vehicles. Here, we propose a novel method for semantic segmentation of road scenes that uses a modified pyramid pooling and multistage parsing to handle detection of small objects. The proposed method achieves excellent results, exceeding the accuracy of 78,61 % of mIoU with 16 fps using a single Titan X 1080 GPU.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
