# YOLOv8 with Post-Processing for Small Object Detection Enhancement
## 6 Conclusions  
This study proposed a novel algorithm to enhance small object detection performance by integrating the lightweight YOLOv8n model with the CARAFE module. This combination leveraged the strengths of both components: the YOLOv8 model for its efficient object detection capabilities and the CARAFE module for its content-aware upsampling technique. By adopting this approach, the algorithm improved the basic detection capabilities of the YOLOv8 model. It effectively captured surrounding contextual information, mitigating information loss during upsampling and preserving detailed features of small objects. The study introduced reliability-based filtering and bounding box expansion techniques to strengthen small object detection further. These methods facilitated the additional detection of small objects with low confidence scores. Expanding the bounding box allowed the algorithm to capture contextual information around the object, enhancing detection accuracy by re-detecting objects within the expanded area. A mechanic was implemented to check for duplicate processing to optimize the process, thereby minimizing unnecessary operations and increasing algorithm efficiency. These enhancements made the proposed algorithm more practical for applications where accurate small object detection is critical. Experimental results demonstrated that the proposed algorithm significantly outperformed the existing YOLOv8n model on various object detection datasets. Notably, detection accuracy improved markedly when identifying small objects with low reliability and performing re-detection. This validated the effectiveness of the contextual information obtained through the bounding box expansion and the content-aware upsampling technique of the CARAFE module.  
However, using bounding box expansion, the crop-based re-detection approach incurs additional computational cost. Optimization is necessary to improve efficiency in real-time processing environments. Nevertheless, the proposed method is advantageous compared to traditional slicing techniques, as the computational cost does not increase linearly. Additional operations are only performed when the model encounters objects with uncertain classifications. In high-risk scenarios, such as fire detection, early and accurate identification is crucial despite the added computational expense. Therefore, the algorithm ensures timely and reliable detection, potentially preventing severe consequences. Moreover, the area management technique designed to prevent duplicate processing is expected to enable more refined improvements in complex scenes. Future research could explore integrating attention mechanisms to allocate computational resources more effectively to critical areas or adopting multi-resolution approaches to capture detailed information about small objects precisely.  
This study demonstrated the potential to significantly improve small object detection by combining the YOLOv8n model with the CARAFE module. The proposed algorithm holds promise for applications requiring accurate small-object detection--such as drone imagery, medical imaging, and unstructured big-data fusion analysis that integrates visual streams with other heterogeneous data sources. This study is expected to guide future research and applications in object detection by providing a foundation for practical performance improvements.  
Conceptualization, J.R.; Methodology, J.R. and D.K.; Software, J.R. and S.C.; Visualization, J.R. and S.C.; Supervision, D.K.; Validation, D.K. and S.C.; Writing--original draft preparation, J.R.; Writing--review and editing, D.K. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the specialized university program for confluence analysis of Weather and Climate Data of the Korea Meteorological Institute (KMI), funded by the Korean government (KMA).