## 6 Conclusions  
This paper found, in the training process, that the detection of small targets is different from other targets and it needs targeted optimization. The bottom layer of the backbone feature extraction network is more suitable for the extraction of small target features because the information of small targets will be gradually lost in the progressive process of the network. Therefore, small targets are not suitable for network structures that are too deep. The a priori frame of the small target is also designed to be close to the size of the small target to ensure the accuracy of the regression. The attention mechanism is a parallel structure in the feature network, which can be inserted in the head and tail, but the enhancement effect in the head is not as good as that in the tail. The insertion process in the middle will destroy the network structure, and the pre-training cannot be used. If the pre-training is not used, it can be inserted in the middle. Therefore, the insertion process in the tail is a good choice.  
The experimental results show that the detection accuracy of this paper is significantly improved. After using the pre-training method, the training speed of the model is very impressive, but the detection speed is slow, only 8 FPS. In terms of the detection effect, the recognition effect of images with light interference from clouds in the air is better than that of images with many buildings on the ground, and many drone targets that are not easily detectable by the human eye can be directly detected.  
There are still shortcomings in the detection in this paper: (1) ground buildings and vehicles interfere with the detection effect; (2) the detection speed still needs to be improved compared with the \"Yolo series\" of algorithms, and these shortcomings will be slowly addressed in the following research.  
In the current trend of deep learning, many algorithms often need a large number of datasets to train models. The datasets required by some large models may reach the TB level, but they will also contain a lot of invalid information. The pre-training method is more efficient than self-training when dealing with small datasets and has a better effect when performing the same training task. Moreover, the pre-training method is also very popular in the current detection field. However, when there are a large number of labeled datasets, the effect of self-training is better than the pre-training, but it will also increase a lot of costs. Therefore, the selection of training methods should depend on the users' own applications. At the same time, cutting-edge experts are also very optimistic that high-quality small datasets can replace large datasets to train high-quality models in the future.  
To the best of our knowledge, we believe that the pre-training approach can reduce the threshold of deep learning and allows more people to understand the technology of deep learning. Therefore, the pre-training approach still has good development prospects.  
Conceptualization, J.C., Y.L. and G.L.; methodology, J.C. and G.L.; software, J.C.; validation, J.C., Y.L. and G.L.; formal analysis, Y.L.; investigation, J.C. and G.L.; resources, J.P.; data curation, J.C.; writing--original draft preparation, J.C. and G.L; writing--review and editing, J.L. and J.H.; visualization, J.C.; supervision, Y.L.; project administration, G.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
[PERSON] would like to thank the Innovation Foundation of Changchun University of Science 358 and Technology (XJILG-2018-07) and the Education Department of Jilin Province, China 359 (JKJH20210838 KJ). [PERSON] is grateful for financial support through a Marie Sklodowska Curie Fellowship, H2020-MSCA-IF-2020-101022219.