A small target detection algorithm based on super-resolution reconstruction is proposed to solve the problem of low detection accuracy of small targets occupying a few pixels. First, a high-resolution image is segmented via image preprocessing and sub-images containing targets are filtered out. Second, a super-resolution sharpening enhancement module is constructed, and the sharpening image and sharpening loss are introduced to obtain high-resolution sub-images with clearer edges. Subsequently, a multi-scale sharpening target detection module is used to detect the target; it uses an edge-sharpening model to further enhance the image edges of the deep feature layer to compensate for the loss in details due to deep convolution. Finally, the small-target detection results are returned in the original image based on the sub-image number used to complete small target image detection. The proposed detection algorithm is then verified using the PASCAL VOC and COCO 2017 datasets, where the average accuracies (mAP) are 85.3% and 54.0%, respectively. Moreover, the small target detection accuracy of the COCO dataset is 43.5%, which is 9.7 percentage points higher than the suboptimal value. Therefore, the proposed algorithm can effectively reduce the number of times small targets are missed during detection, thus improving the detection accuracy.