{"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/detecting-traffic-lights-by-single-shot","title":"Detecting Traffic Lights by Single Shot Detection","arxiv_id":"1805.02523","date":"2018-05-07","proceeding":null,"authors":["Julian Müller","Klaus Dietmayer"],"abstract":"Recent improvements in object detection are driven by the success of\nconvolutional neural networks (CNN). They are able to learn rich features\noutperforming hand-crafted features. So far, research in traffic light\ndetection mainly focused on hand-crafted features, such as color, shape or\nbrightness of the traffic light bulb. This paper presents a deep learning\napproach for accurate traffic light detection in adapting a single shot\ndetection (SSD) approach. SSD performs object proposals creation and\nclassification using a single CNN. The original SSD struggles in detecting very\nsmall objects, which is essential for traffic light detection. By our\nadaptations it is possible to detect objects much smaller than ten pixels\nwithout increasing the input image size. We present an extensive evaluation on\nthe DriveU Traffic Light Dataset (DTLD). We reach both, high accuracy and low\nfalse positive rates. The trained model is real-time capable with ten frames\nper second on a Nvidia Titan Xp. Code has been made available at\nhttps://github.com/julimueller/tl_ssd.","url_abs":"http://arxiv.org/abs/1805.02523v3","url_pdf":"http://arxiv.org/pdf/1805.02523v3.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":"detecting-traffic-lights-by-single-shot","repo_url":"https://github.com/julimueller/tl_ssd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"detecting-traffic-lights-by-single-shot","repo_url":"https://github.com/sovit-123/traffic-light-detection-using-yolov3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"ssd","method_name":"SSD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}