提出多轮并行查询机制，提升目标检测对复杂场景的感知能力
MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism
- 采用并行多轮查询机制，让目标查询充分吸收图像特征
- 在COCO上超越DINO和Relation-DETR，AP提升2.3和0.6
- 适用于小目标、遮挡、背景混杂等复杂场景检测
针对现有DETR类模型级联解码器架构的局限性——目标查询仅沿级联方向更新，难以充分获取图像特征信息，本文提出一种新型解码器架构，引入并行多轮查询（MI）机制。该机制使目标查询能更全面地学习图像特征，显著增强模型对自然场景中极小目标、严重遮挡及与背景混淆等挑战的应对能力。所提出的MI-DETR模型在不同骨干网络和训练周期下均优于所有现有DETR类模型，在ResNet-50主干下相比代表性模型DINO和最先进模型Relation-DETR分别实现+2.3 AP和+0.6 AP的性能提升。一系列诊断与可视化实验验证了MI机制的有效性、合理性与可解释性。
原文摘要 · Abstract (English)
Based on analyzing the character of cascaded decoder architecture commonly adopted in existing DETR-like models, this paper proposes a new decoder architecture. The cascaded decoder architecture constrains object queries to update in the cascaded direction, only enabling object queries to learn relatively-limited information from image features. However, the challenges for object detection in natural scenes (e.g., extremely-small, heavily-occluded, and confusingly mixed with the background) require an object detection model to fully utilize image features, which motivates us to propose a new decoder architecture with the parallel Multi-time Inquiries (MI) mechanism. MI enables object queries to learn more comprehensive information, and our MI based model, MI-DETR, outperforms all existing DETR-like models on COCO benchmark under different backbones and training epochs, achieving +2.3 AP and +0.6 AP improvements compared to the most representative model DINO and SOTA model Relation-DETR under ResNet-50 backbone. In addition, a series of diagnostic and visualization experiments demonstrate the effectiveness, rationality, and interpretability of MI.
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