实时检测 | ViLab https://vilab.team/tag/%E5%AE%9E%E6%97%B6%E6%A3%80%E6%B5%8B/ 实时检测 Hugo Blox Builder (https://hugoblox.com)en-usTue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/media/icon_hu2896232876136423579.png 实时检测 https://vilab.team/tag/%E5%AE%9E%E6%97%B6%E6%A3%80%E6%B5%8B/ RiO-DETR: DETR for Real-time Oriented Object Detection https://vilab.team/publication/rio-detr-detr-for-real-time-oriented-object-detection/ Tue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/publication/rio-detr-detr-for-real-time-oriented-object-detection/ <p>本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。</p> 祝贺实验室科研成果发表于 ECCV 2026！ https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/ Tue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/ <p>热烈祝贺胡张驰同学！论文《RiO-DETR: DETR for Real-time Oriented Object Detection》已发表在 ECCV 2026 Oral。</p> <h2 id="rio-detr-detr-for-real-time-oriented-object-detection">RiO-DETR: DETR for Real-time Oriented Object Detection</h2> <p><strong>祝贺胡张驰同学！该论文已发表在 ECCV 2026。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="RiO-DETR: DETR for Real-time Oriented Object Detection" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu3774487554520496472.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu7272795672146682259.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu2318532274489056445.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu3774487554520496472.webp" width="760" height="393" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Zhangchi Hu、Yifan Zhao、Yansong Peng、Wenzhang Sun、Xiangchen Yin、Jie Chen、Peixi Wu、Hebei Li、Xinghao Wang、Dongsheng Jiang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> ECCV 2026</li> <li><strong>发表时间：</strong> 2026年3月10日</li> <li><strong>相关链接：</strong> <a href="https://arxiv.org/abs/2603.09411" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2603.09411" target="_blank" rel="noopener">PDF</a> · <a href="https://github.com/RicePasteM/RiO-DETR" target="_blank" rel="noopener">代码</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。</p> 祝贺实验室科研成果发表于 ECCV 2026！ https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/ Tue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/ <p>热烈祝贺胡张驰同学！论文《RiO-DETR: DETR for Real-time Oriented Object Detection》已发表在 ECCV 2026。</p> <h2 id="rio-detr-detr-for-real-time-oriented-object-detection">RiO-DETR: DETR for Real-time Oriented Object Detection</h2> <p><strong>祝贺胡张驰同学！该论文已发表在 ECCV 2026。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="RiO-DETR: DETR for Real-time Oriented Object Detection" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu3774487554520496472.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu7272795672146682259.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu2318532274489056445.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu3774487554520496472.webp" width="760" height="393" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Zhangchi Hu、Yifan Zhao、Yansong Peng、Wenzhang Sun、Xiangchen Yin、Jie Chen、Peixi Wu、Hebei Li、Xinghao Wang、Dongsheng Jiang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> ECCV 2026</li> <li><strong>发表时间：</strong> 2026年3月10日</li> <li><strong>相关链接：</strong> <a href="https://arxiv.org/abs/2603.09411" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2603.09411" target="_blank" rel="noopener">PDF</a> · <a href="https://github.com/RicePasteM/RiO-DETR" target="_blank" rel="noopener">代码</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。</p> D-FINE: Redefine regression task of DETRs as fine-grained distribution refinement https://vilab.team/publication/d-fine-redefine-regression-task-of-detrs-as-fine-grained-dis/ Thu, 01 May 2025 00:00:00 +0000 https://vilab.team/publication/d-fine-redefine-regression-task-of-detrs-as-fine-grained-dis/ <p>D-FINE是一种实时目标检测器，通过重新定义DETR中的边界框回归任务实现高精度定位。其核心包含细粒度分布细化（FDR）和全局最优定位自蒸馏（GO-LSD）。FDR将回归从预测固定坐标改为迭代细化概率分布，提供细粒度中间表示；GO-LSD通过自蒸馏将定位知识从最终层传递到浅层，并简化深层残差预测。在COCO上达到54.0%/55.8% AP，124/78 FPS，预训练后达57.1%/59.3% AP，超越现有实时检测器，并显著提升多种DETR模型性能。</p> 祝贺实验室科研成果发表于 ICLR 2025！ https://vilab.team/event/publication-news-06e581b9272339ee/ Thu, 01 May 2025 00:00:00 +0000 https://vilab.team/event/publication-news-06e581b9272339ee/ <p>热烈祝贺彭岩松同学！论文《D-FINE: Redefine regression task of DETRs as fine-grained distribution refinement》已发表在 <em>ICLR 2025</em>。</p> <h2 id="d-fine-redefine-regression-task-of-detrs-as-fine-grained-distribution-refinement">D-FINE: Redefine regression task of DETRs as fine-grained distribution refinement</h2> <p><strong>祝贺彭岩松同学！该论文已发表在 <em>ICLR</em>。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="D-FINE: Redefine regression task of DETRs as fine-grained distribution refinement" srcset=" /event/publication-news-06e581b9272339ee/images/paper-01_hu14730101056268865597.webp 400w, /event/publication-news-06e581b9272339ee/images/paper-01_hu1666356483600992880.webp 760w, /event/publication-news-06e581b9272339ee/images/paper-01_hu15779176642512148333.webp 1200w" src="https://vilab.team/event/publication-news-06e581b9272339ee/images/paper-01_hu14730101056268865597.webp" width="760" height="314" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Yansong Peng、Hebei Li、Peixi Wu、Yueyi Zhang、Xiaoyan Sun、Feng Wu</li> <li><strong>发表载体：</strong> <em>ICLR</em></li> <li><strong>发表时间：</strong> 2025年5月1日</li> <li><strong>相关链接：</strong> <a href="https://proceedings.iclr.cc/paper_files/paper/2025/hash/6cf58a87e3097e7d1f9be3e8693a93de-Abstract-Conference.html" target="_blank" rel="noopener">论文链接</a> · <a href="https://proceedings.iclr.cc/paper_files/paper/2025/file/6cf58a87e3097e7d1f9be3e8693a93de-Paper-Conference.pdf" target="_blank" rel="noopener">PDF</a> · <a href="https://github.com/Peterande/D-FINE" target="_blank" rel="noopener">代码</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>D-FINE是一种实时目标检测器，通过重新定义DETR中的边界框回归任务实现高精度定位。其核心包含细粒度分布细化（FDR）和全局最优定位自蒸馏（GO-LSD）。FDR将回归从预测固定坐标改为迭代细化概率分布，提供细粒度中间表示；GO-LSD通过自蒸馏将定位知识从最终层传递到浅层，并简化深层残差预测。在COCO上达到54.0%/55.8% AP，124/78 FPS，预训练后达57.1%/59.3% AP，超越现有实时检测器，并显著提升多种DETR模型性能。</p> Scene adaptive sparse transformer for event-based object detection https://vilab.team/publication/scene-adaptive-sparse-transformer-for-event-based-object-det/ Sun, 16 Jun 2024 00:00:00 +0000 https://vilab.team/publication/scene-adaptive-sparse-transformer-for-event-based-object-det/ <p>本文针对事件相机目标检测中Transformer计算成本过高的问题，提出场景自适应稀疏Transformer（SAST）。该方法通过窗口-令牌协同稀疏化与场景特定稀疏优化，在保持低计算量的同时实现高检测性能，并能根据场景复杂度自适应调整稀疏程度，有效平衡了检测精度与效率。</p> Better and faster: Adaptive event conversion for event-based object detection https://vilab.team/publication/better-and-faster-adaptive-event-conversion-for-event-based-/ Mon, 26 Jun 2023 00:00:00 +0000 https://vilab.team/publication/better-and-faster-adaptive-event-conversion-for-event-based-/ <p>本文针对事件相机目标检测任务，提出高效事件表示Hyper Histogram，充分保留事件极性与时间信息；设计自适应事件转换模块AEC，基于事件密度通过自适应队列将事件流转换为超直方图，并适配现有帧基检测器；还提出事件增强方法Shadow Mosaic，提升样本多样性与泛化能力。在YOLOv5、Deformable-DETR和RetinaNet上验证，在1Mpx、Gen1和MVSEC-NIGHTL21数据集上取得显著优势，且推理速度快。</p>