{"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/feature-fused-ssd-fast-detection-for-small","title":"Feature-Fused SSD: Fast Detection for Small Objects","arxiv_id":"1709.05054","date":"2017-09-15","proceeding":null,"authors":["Guimei Cao","Xuemei Xie","Wenzhe Yang","Quan Liao","Guangming Shi","Jinjian Wu"],"abstract":"Small objects detection is a challenging task in computer vision due to its\nlimited resolution and information. In order to solve this problem, the\nmajority of existing methods sacrifice speed for improvement in accuracy. In\nthis paper, we aim to detect small objects at a fast speed, using the best\nobject detector Single Shot Multibox Detector (SSD) with respect to\naccuracy-vs-speed trade-off as base architecture. We propose a multi-level\nfeature fusion method for introducing contextual information in SSD, in order\nto improve the accuracy for small objects. In detailed fusion operation, we\ndesign two feature fusion modules, concatenation module and element-sum module,\ndifferent in the way of adding contextual information. Experimental results\nshow that these two fusion modules obtain higher mAP on PASCALVOC2007 than\nbaseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points\nimprovement on some smallobjects categories. The testing speed of them is 43\nand 40 FPS respectively, superior to the state of the art Deconvolutional\nsingle shot detector (DSSD) by 29.4 and 26.4 FPS. Code is available at\nhttps://github.com/wnzhyee/Feature-Fused-SSD. Keywords: small object detection,\nfeature fusion, real-time, single shot multi-box detector","url_abs":"http://arxiv.org/abs/1709.05054v3","url_pdf":"http://arxiv.org/pdf/1709.05054v3.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":"feature-fused-ssd-fast-detection-for-small","repo_url":"https://github.com/wnzhyee/Feature-Fused-SSD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"small-object-detection","task_name":"Small 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":"speed","method_name":"SPEED"},{"method_slug":"ssd","method_name":"SSD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1709.05054","atlas_url":"https://app.syntology.ai/?focus=1709.05054","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}