{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/speeding-up-semantic-segmentation-for","title":"Speeding up Semantic Segmentation for Autonomous Driving","arxiv_id":null,"date":"2016-06-01","proceeding":null,"authors":["Michael Treml","Jose A. Arjona-Medina","Thomas Unterthiner","Rupesh Durgesh","Felix Friedmann","Peter Schuberth","Andreas Mayr","Martin Heusel","Markus Hofmarcher","Michael Widrich","Bernhard Nessler","Sepp Hochreiter"],"abstract":"Deep learning has considerably improved semantic image segmentation. However, its high accuracy is traded against larger computational costs which makes it unsuitable for embedded devices in self-driving cars. We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices. The architecture consists of ELU activation functions, a SqueezeNet-like encoder, followed by parallel dilated convolutions, and a decoder with SharpMask-like refinement modules. On the Cityscapes dataset, the new network achieves higher segmentation accuracy than other networks that are tailored to embedded devices. Simultaneously the frame-rate is still sufficiently high for the deployment in autonomous vehicles.","url_abs":"https://www.ixueshu.com/document/a8d44f655c02eeb8cf5a342f4ab350c1.html","url_pdf":"https://www.ixueshu.com/document/a8d44f655c02eeb8cf5a342f4ab350c1.html","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"speeding-up-semantic-segmentation-for","repo_url":"https://github.com/klickmal/speeding_up_semantic_segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"speeding-up-semantic-segmentation-for","repo_url":"https://github.com/zh320/realtime-semantic-segmentation-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}