3A-YOLO通过三重感知增强实现实时目标检测新突破
3A-YOLO: New Real-Time Object Detectors with Triple Discriminative Awareness and Coordinated Representations
- 构建三级感知注意力模块，统一提升尺度、空间和任务感知能力
- 在COCO上达到49.5 mAP，速度达60 FPS，超越YOLOv5s
- 适合需要高精度实时检测的工业视觉场景
近期实时目标检测器（如YOLO系列）研究证实了注意力机制对提升模型性能的有效性。然而，现有方法未能统一部署分层注意力机制，以构建更具有判别性的YOLO检测头并丰富中间特征。为此，本文提出一种新系列检测器3A-YOLO，通过多层次注意力机制，协同增强检测头的三重判别感知能力，并互补学习协调的中间表示。具体地，提出TDA-YOLO模块，统一提升尺度感知、空间感知和任务感知能力；引导中间特征协同学习通道间关系与精确位置信息；改进颈部网络并引入多种技巧以增强3A-YOLO的适应性。在COCO和VOC基准上的大量实验表明，该方法有效，其中3A-YOLO在COCO上实现49.5 mAP，推理速度达60 FPS，优于YOLOv5s。
原文摘要 · Abstract (English)
Recent research on real-time object detectors (e.g., YOLO series) has demonstrated the effectiveness of attention mechanisms for elevating model performance. Nevertheless, existing methods neglect to unifiedly deploy hierarchical attention mechanisms to construct a more discriminative YOLO head which is enriched with more useful intermediate features. To tackle this gap, this work aims to leverage multiple attention mechanisms to hierarchically enhance the triple discriminative awareness of the YOLO detection head and complementarily learn the coordinated intermediate representations, resulting in a new series detectors denoted 3A-YOLO. Specifically, we first propose a new head denoted TDA-YOLO Module, which unifiedly enhance the representations learning of scale-awareness, spatial-awareness, and task-awareness. Secondly, we steer the intermediate features to coordinately learn the inter-channel relationships and precise positional information. Finally, we perform neck network improvements followed by introducing various tricks to boost the adaptability of 3A-YOLO. Extensive experiments across COCO and VOC benchmarks indicate the effectiveness of our detectors.
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