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.