{"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/multispectral-fusion-for-object-detection","title":"Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks","arxiv_id":"2009.12664","date":"2020-09-26","proceeding":null,"authors":["Heng Zhang","Elisa Fromont","Sébastien Lefevre","Bruno Avignon"],"abstract":"Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods.","url_abs":"https://arxiv.org/abs/2009.12664v1","url_pdf":"https://arxiv.org/pdf/2009.12664v1.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":"multispectral-fusion-for-object-detection","repo_url":"https://github.com/ZHANGHeng19931123/CFR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multispectral-object-detection","task_name":"Multispectral Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"CFR_3 (VGG16)","rank_in_archive_order":15,"of":18,"metrics":{"mAP50":"72.4%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"Halfway Fusion (VGG16)","rank_in_archive_order":16,"of":18,"metrics":{"mAP50":"71.2%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-kaist","task":"Multispectral Object Detection","dataset":"KAIST Multispectral Pedestrian Detection Benchmark","model":"CFR","rank_in_archive_order":16,"of":17,"metrics":{"Reasonable Miss Rate":"6.13"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2009.12664","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}