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.