【摘要】 为了提升目标检测算法在多尺度学习方面的能力，尤其是对小目标的检测能力，本文提出了一种基于改进YOLOv5的超分辨率和多尺度融合目标检测算法。首先，该算法使用子像素卷积代替原YOLOv5模型的上采样操作，提高图像的分辨率，并尽可能保留小目标的信息。其次，使用并行快速多尺度融合(parallel fast multi-scale fusion, PFMF)模块实现深层特征和浅层特征的双向融合，将原YOLOv5算法的3尺度预测升级为4尺度预测，以此提高模型多尺度特征学习能力和对小目标的检测效果。实验结果表明，与YOLOv5s相比，改进后的模型在PASCAL VOC数据集中，mAP@0.5提高了2.8个百分点，mAP@0.5∶0.95提高了3.5个百分点；在MS COCO数据集中，mAP@0.5提高了4.3个百分点，mAP@0.5∶0.95提高了5.2个百分点。改进后的YOLOv5模型在多尺度检测，尤其是小目标的检测效果方面得到了提升，并具有一定的应用价值。更多还原

【Abstract】 To enhance the multi-scale learning capacity of target detection algorithms, particularly for small targets, this paper proposes a super-resolution and multi-scale fusion target detection algorithm based on an improved YOLOv5 framework.Firstly, instead of the up-sampling operation of the original YOLOv5 model, the algorithm utilizes sub-pixel convolution to enhance the image resolution and preserve the information of small targets to the greatest extent possible.Secondly, the algorithm utilizes the parallel fast multi-scale fusion(PFMF) module to achieve two-way fusion of deep and shallow features.This upgrade from the original YOLOv5 algorithm′s 3-scale prediction to 4-scale prediction improves the model′s ability to learn multi-scale features and detect small targets.The experimental results demonstrate that compared with YOLOv5s, the improved model achieves a 2.8% and 3.5% increase in mAP@0.5 and mAP@0.5∶0.95,respectively, on the PASCAL VOC dataset.Similarly, on the MS COCO dataset, the improved model achieves a 4.3% and 5.2% increase in mAP@0.5 and mAP@0.5∶0.95,respectively.The experiments demonstrate the improved YOLOv5 model’s enhanced capability in multi-scale detection, particularly for small targets, and indicate its potential practical value.更多还原