{"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-raw-object-detection-in-diverse","title":"Towards RAW Object Detection in Diverse Conditions","arxiv_id":"2411.15678","date":"2024-11-24","proceeding":"CVPR 2025 1","authors":["Zhong-Yu Li","Xin Jin","Boyuan Sun","Chun-Le Guo","Ming-Ming Cheng"],"abstract":"Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might lose crucial information for detection, especially under complex light and weather conditions. We introduce the AODRaw dataset, which offers 7,785 high-resolution real RAW images with 135,601 annotated instances spanning 62 categories, capturing a broad range of indoor and outdoor scenes under 9 distinct light and weather conditions. Based on AODRaw that supports RAW and sRGB object detection, we provide a comprehensive benchmark for evaluating current detection methods. We find that sRGB pre-training constrains the potential of RAW object detection due to the domain gap between sRGB and RAW, prompting us to directly pre-train on the RAW domain. However, it is harder for RAW pre-training to learn rich representations than sRGB pre-training due to the camera noise. To assist RAW pre-training, we distill the knowledge from an off-the-shelf model pre-trained on the sRGB domain. As a result, we achieve substantial improvements under diverse and adverse conditions without relying on extra pre-processing modules. Code and dataset are available at https://github.com/lzyhha/AODRaw.","url_abs":"https://arxiv.org/abs/2411.15678v1","url_pdf":"https://arxiv.org/pdf/2411.15678v1.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-raw-object-detection-in-diverse","repo_url":"https://github.com/lzyhha/aodraw","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"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":[{"slug":"aodraw","name":"AODRaw","full_name":"Adverse condition Object Detection with RAW images"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-aodraw","task":"Object Detection","dataset":"AODRaw","model":"Cascade RCNN (ConvNext-T, RAW pre-training)","rank_in_archive_order":1,"of":1,"metrics":{" box AP":"34.8"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2411.15678","atlas_url":"https://app.syntology.ai/?focus=2411.15678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.15678"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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