## 5 Conclusions  
In this paper, we proposed a new network structure called DDU (Downsample Difference Upsample), which utilizes the methods of upsampling and downsampling, as well as complementary thinking, in the Neck layer of a deep learning network to enhance the recognition features of small targets and further improve the richness of recognition features. As a result, it addresses the issue of low accuracy in recognizing small target objects and effectively enhances the feature richness of recognition. Additionally, in order to address the recognition of objects of different sizes in image recognition, we introduced a new attention mechanism called PNOC (protecting the number of channels) to ensure that small target features and common objects were not lost during the channel integration process, thereby improving the recognition accuracy. We conducted experimental validation on the PASCAL-VOC dataset. Through experimental comparisons, we found that our accuracy surpassed other one-stage classical algorithms. Ablation experiments demonstrated positive improvements in recognition for both of the designed modules. Compared to the original YOLOv8 code, our approach achieved a 0.7% increase in accuracy. Furthermore, we applied our method to the fine-grained MAR20 dataset and observed superior performance compared to other classical algorithms, with a notable 6.6% improvement over the closely related TSD algorithm. Additionally, our proposed framework falls under the category of hierarchical detection methods, which exhibit excellent timeliness and demonstrate good adaptability and universality in scientific research applications.  
This paper proposed a simple sampling algorithm to address the issue of easily lost small target features by comparing multilayer information discrepancies. It then focused on obtaining specific features for these features and utilized the attention mechanism to increase the weight of easily lost parts, thereby partially overcoming the problem of feature loss. The algorithm explores work that predecessors have not attempted in the feature extraction part while also leveraging existing prior knowledge. Benefiting from the accumulation of previous research achievements, the algorithm demonstrates feasibility and interpretability during the theoretical reasoning phase prior to experimental implementation. Through experiments comparing algorithms with the PASCAL-VOC dataset and MAR20 dataset, the theory and experiments have been correspondingly validated, thereby enhancing the interpretability of deep learning. It is evident that this paper exhibits a certain degree of innovativeness in the overall model.  
Methodology, Q.F. and H.L.; software, Q.F. and H.L.; validation, H.L.; data curation, H.L.; writing--review and editing, Q.F., X.T. and W.D.; visualization, Q.F., H.L. and X.T.; supervision, Q.F. and W.D.; project administration, W.D.; funding acquisition, X.T. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by National Natural Science Foundation of China (62375260) and Jilin Province Innovation and Entrepreneurial Talent Project (2023 QN17). This research was funded by Youth innovation promotion association cas (No. 2021221).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available at [[https://doi.org/10.11834/jrs.20222139](https://doi.org/10.11834/jrs.20222139)]([https://doi.org/10.11834/jrs.20222139](https://doi.org/10.11834/jrs.20222139)), reference number [15].  
**Acknowledgments:** We would like to thank the anonymous reviewers for their valuable suggestions, which have helped us to improve the manuscript greatly.  
**Conflicts of Interest:** The authors declare no conflicts of interest.