# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 5 Conclusions  
This study proposes a small-target detection method based on an attention mechanism; the proposed method has a higher detection accuracy and can perform a more detailed segmentation compared to the baseline. The proposed method was improved based on  
Figure 11: Real apron detection results.  
the advanced and representative Mask Scoring R-CNN algorithm. By introducing an attention module, using more reasonable feature fusion, and more effective detection branches, the effectiveness and accuracy of the detection of small airport targets were improved significantly. We carried out a series of ablation and comparative experiments on a small-target dataset. The major conclusions are as follows.  
1. Considering that there are currently almost no datasets suitable for small-target detection, we produced a small-target dataset for aprons that meets the definition of small targets from both relative and absolute scales.
2. Considering that the targets are too small and their features are difficult to extract, an attention module was used to effectively focus on the features and thereby improve the detection accuracy. Furthermore, using a feature fusion module helped achieve a more effective multiscale feature fusion of small targets and a new classifier head was used for a more efficient detection of single classes of small targets and for reducing model complexity.
3. Considering the difficulties associated with small-target detection in practical applications, a suitable method is proposed. Experiments show that the proposed method achieved improved detection accuracy compared with the baseline. The accuracy in the scale range of (0, 0.01) was improved by 16%, and in the range of (0.01, 0.02), by 15.15% compared with the Mask Scoring R-CNN.  
Although the improved method was initially validated by ablation and comparative experiments with promising performance, there is still much room for further development, which is summarised as follows.  
1. The first task will be to further optimise the model and strengthen the practical application of the model, such as decreasing inference speed by reducing model parameters.
2. Since the targets of this study are pedestrians on the airport surface, the targets are single; thus, the next step can be to expand the dataset for other small-target detection tasks.  
Conceptualisation, H.L.; methodology, H.L.; software, H.L.; validation, H.L.; formal analysis, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L., M.D. and A.N.K.; visualisation, H.L. and S.L.; supervision, M.D., S.G. and Y.X.; project administration, M.D. All authors have read and agreed to the published version of the manuscript.  
This work is co-supported by the National Natural Science Foundation of China (No. U2033201 and U2033216). It is also supported by the Opening Project of Civil Aviation Satellite Application Engineering Technology Research Center (RCCASA-2022003) and Nanjing University of Aeronautics and Astronautics Innovation Program Project (ccxjh20220736).  
Not applicable.  
All of the grants for this manuscript are still in the research phase, and some research data or key codes are currently limited to disclosure within the project team. However, some data involved in this study can be provided. If necessary, you can contact [PERSON], [PERSON] via email ([EMAIL_ADDRESS]) to obtain the Baidu Netdisk (Baidu Cloud) URL link and then download the files you need. Once the link is gone, you can contact the authors of this article to obtain the latest link.  
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The authors declare no conflict of interest.