基于非局部融合的多尺度目标检测研究

Multi‑scale Object Detection Based on Non‑local Feature Fusion

针对现有的多尺度目标检测模型在面对尺度变换和遮挡场景时所使用的融合方法融合不充分,且没有捕捉长距离依赖关系的问题,本文设计了通道融合增强模块和非局部特征交互模块,用于学习不同通道特征之间的相关性和捕捉特征图之间的长距离依赖关系。此外,针对当前检测架构都是基于单金字塔检测结构,存在信息丢失的情况,设计了双金字塔结构,并将提出的融合方法与双金字塔结构结合,在保留原始特征信息的基础上,补充融合后的特征信息。实验结果表明,提出的方法在公共数据集KITTI与PASCAL VOC上与其他先进工作相比具有更高的检测精度,证明了该方法在目标检测任务中的有效性。

Aiming at the problem that the fusion method used by the existing multi‑scale object detection model in the face of scale variation and occlusion scene is not sufficient,and does not capture the long‑distance dependency relationship,channel feature fusion aggregation module and non‑local feature interaction module are designed to learn the correlation between different channel features and capture the long‑distance dependence between feature maps.In addition,the current detection architecture is based on single pyramid detection structure,which exists information loss.In this paper,a double pyramid structure is designed,and the proposed fusion method is combined with the double feature pyramid structure to supplement the fusion feature information on the basis of preserving the original feature information.Experimental results on public datasets KITTI and PASCAL VOC show that the proposed method has higher detection accuracy than other advanced work,proving its effectiveness in object detection task.