# YOLOv8 with Post-Processing for Small Object Detection Enhancement
## 2 Related Works  
Recent studies have explored various upsampling methods to enhance the performance of object detection models. Among these, the CARAFE module has emerged as a highly effective enhancement to the upsampling process in the YOLOv8n model. Unlike traditional fixed-kernel upsampling techniques, CARAFE generates adaptive upsampling kernels based on the content of the input feature map, enabling more precise feature map restoration [22].  
The primary advantage of CARAFE is its ability to aggregate contextual information through a large receptive field, minimizing information loss during the upsampling process. This enhancement significantly improves the model's performance in detecting small objects and handling complex backgrounds. For instance, a study integrating CARAFE into the neck of the YOLOv8n model demonstrated superior detection performance compared to conventional upsampling methods, with notable improvements in small object detection accuracy.  
Additionally, CARAFE offers high computational efficiency and a lightweight structure, making it ideal for real-time detection tasks. By replacing the original upsampling module in the YOLOv8n model with CARAFE, performance improvements were observed in detecting small and multi-scale objects, enhancing the overall detection accuracy and consistency.  
These findings underscore CARAFE's potential to minimize information loss during upsampling while effectively detecting objects across varying scales. Building on this existing research, the current study aims to further enhance object detection performance by integrating the CARAFE module into both the image processing pipeline and the neck of the YOLOv8n model.  
Another approach to improving object detection involves dividing the input image into smaller segments (slices), performing model inference on each slice individually, and then merging the results into a single image prediction. The slicing-aided hyperinference  
Figure 1: Overall flowchart of the proposed detection method.  
(SAHI) algorithm exemplifies this technique. SAHI divides the image into slices, applies an object detection algorithm to each segment, and combines the results to generate a final prediction [20].  
A key feature of SAHI is its compatibility with the existing object detection models, enabling integration without requiring separate retraining. Furthermore, it enhances small object detection by increasing the relative pixel size of small objects through image segmentation. Experimental results show that applying SAHI improves average precision (AP) by up to 6.8% across several models, including fully convolutional one-stage object detection (FCOS), VarifocalNet (VFNet), and task-aligned one-stage object detection (TOOD). Further improvements, up to 14.5%, were achieved by incorporating patch overlap and additional fine-tuning.  
However, SAHI has some limitations. The computation time increases linearly during image segmentation and detection, and while memory requirements are constant, handling high-resolution images still demands significant computational resources. Additionally, with patch-based detection, challenges remain at the inter-patch boundaries, and performance degradation occurs when detecting large objects, necessitating further investigation.