YOLO-PRO通过双模块提升目标检测精度，兼顾效率与边缘部署
YOLO-PRO: Enhancing Instance-Specific Object Detection with Full-Channel Global Self-Attention
- 引入全通道全局自注意力的实例感知瓶颈模块
- 在COCO上比YOLOv8高1.0-1.6% AP，比YOLO11高0.1-0.5% AP
- 适合追求高精度且需部署在边缘设备的检测任务
本文针对传统瓶颈结构（因过度依赖批次统计导致实例区分度下降）和解耦头（计算冗余）的局限性，提出两个新模块：全通道全局自注意力的实例感知瓶颈（ISB）与实例感知非对称解耦头（ISADH）。ISB通过融合批次统计与实例特异性特征，重建特征图以建立高效全通道全局注意力机制。ISADH采用非对称解耦架构，通过双流批次-实例表示融合实现分层多维特征整合。在MS-COCO基准上的大量实验表明，将ISB与ISADH协同部署于YOLO-PRO框架，在所有计算规模下均达到当前最优性能。具体而言，YOLO-PRO在N/S/M/L/X各尺度上较YOLOv8提升1.0-1.6% AP，较YOLO11在关键N/M/L/X组提升0.1-0.5% AP，同时保持良好计算效率。本工作为开发适用于边缘设备的高精度检测器提供了实用思路。
原文摘要 · Abstract (English)
This paper addresses the inherent limitations of conventional bottleneck structures (diminished instance discriminability due to overemphasis on batch statistics) and decoupled heads (computational redundancy) in object detection frameworks by proposing two novel modules: the Instance-Specific Bottleneck with full-channel global self-attention (ISB) and the Instance-Specific Asymmetric Decoupled Head (ISADH). The ISB module innovatively reconstructs feature maps to establish an efficient full-channel global attention mechanism through synergistic fusion of batch-statistical and instance-specific features. Complementing this, the ISADH module pioneers an asymmetric decoupled architecture enabling hierarchical multi-dimensional feature integration via dual-stream batch-instance representation fusion. Extensive experiments on the MS-COCO benchmark demonstrate that the coordinated deployment of ISB and ISADH in the YOLO-PRO framework achieves state-of-the-art performance across all computational scales. Specifically, YOLO-PRO surpasses YOLOv8 by 1.0-1.6% AP (N/S/M/L/X scales) and outperforms YOLO11 by 0.1-0.5% AP in critical N/M/L/X groups, while maintaining competitive computational efficiency. This work provides practical insights for developing high-precision detectors deployable on edge devices.
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