{"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/towards-scene-understanding-for-autonomous","title":"Towards Scene Understanding for Autonomous Operations on Airport Aprons","arxiv_id":null,"date":"2022-12-04","proceeding":"Asian Conference on Computer Vision (ACCV) Workshops 2022 12","authors":["Daniel Steininger","Andreas Kriegler","Wolfgang Pointner","Verena Widhalm","Julia Simon","Oliver Zendel"],"abstract":"Enhancing logistics vehicles on airport aprons with assistant and autonomous capabilities offers the potential to significantly increase\r\nsafety and efficiency of operations. However, this research area is still underrepresented compared to other automotive domains, especially regarding available image data, which is essential for training and benchmarking AI-based approaches. To mitigate this gap, we introduce a novel dataset specialized on static and dynamic objects commonly encountered while navigating apron areas. We propose an efficient approach for image acquisition as well as annotation of object instances and environmental\r\nparameters. Furthermore, we derive multiple dataset variants on which we conduct baseline classification and detection experiments. The resulting models are evaluated with respect to their overall performance and robustness against specific environmental conditions. The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.","url_abs":"https://openaccess.thecvf.com/content/ACCV2022W/MLCSA/html/Steininger_Towards_Scene_Understanding_for_Autonomous_Operations_on_Airport_Aprons_ACCVW_2022_paper.html","url_pdf":"https://openaccess.thecvf.com/content/ACCV2022W/MLCSA/papers/Steininger_Towards_Scene_Understanding_for_Autonomous_Operations_on_Airport_Aprons_ACCVW_2022_paper.pdf","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":"towards-scene-understanding-for-autonomous","repo_url":"https://github.com/apronai/apron-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"robust-object-detection","task_name":"Robust Object Detection"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"small-object-detection","task_name":"Small Object Detection"}],"methods":[],"datasets_introduced":[{"slug":"apron-dataset","name":"Apron Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}