让背景信息助力目标检测，提升模型准确率
Don't let the information slip away
- 引入关联机制，融合背景上下文增强检测能力
- 在COCO val2017上达到55.2 mAP，超越YOLOv12和RT-DETRv2
- 适合需要高精度定位的视觉识别任务
实时目标检测近年来发展迅速。以YOLO系列为代表的基于卷积神经网络的目标检测模型广受关注，其中YOLOv12在COCO val2017数据集上达到55.2 mAP的领先性能。同时，基于Transformer的检测模型如DETR及其优化版本RT-DETR也表现出色，其最新迭代RT-DETRv2在该数据集上取得53.4 mAP的成绩。然而，这些模型普遍忽视了背景提供的上下文信息，仅聚焦于前景物体特征。我们认为，背景信息能显著辅助检测任务——例如车辆更可能出现在道路而非办公室，野生动物多见于森林或偏远地区。为此，我们提出Association DETR模型，通过有效利用背景上下文，在COCO val2017数据集上实现当前最优性能。
原文摘要 · Abstract (English)
Real-time object detection has advanced rapidly in recent years. The YOLO series of detectors is among the most well-known CNN-based object detection models and cannot be overlooked. The latest version, YOLOv26, was recently released, while YOLOv12 achieved state-of-the-art (SOTA) performance with 55.2 mAP on the COCO val2017 dataset. Meanwhile, transformer-based object detection models, also known as DEtection TRansformer (DETR), have demonstrated impressive performance. RT-DETR is an outstanding model that outperformed the YOLO series in both speed and accuracy when it was released. Its successor, RT-DETRv2, achieved 53.4 mAP on the COCO val2017 dataset. However, despite their remarkable performance, all these models let information to slip away. They primarily focus on the features of foreground objects while neglecting the contextual information provided by the background. We believe that background information can significantly aid object detection tasks. For example, cars are more likely to appear on roads rather than in offices, while wild animals are more likely to be found in forests or remote areas rather than on busy streets. To address this gap, we propose an object detection model called Association DETR, which achieves state-of-the-art results compared to other object detection models on the COCO val2017 dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。