# YOLOv8 with Post-Processing for Small Object Detection Enhancement
## 3 Proposed Methodology
### Additional Image Processing Steps  
In this study, a CR process-based detection algorithm was developed to enhance the detection performance of small objects, utilizing the YOLOv8n model. The algorithm workflow, illustrated in Figure 4, involves the following main steps (refer to Algorithm 1):  
```
INPUT: Image I, YOLO model M, thresholds T1 = 0.1, T2 = 0.5 OUTPUT: Final detections D_final Begin
1. Initialize:  High confidence list: D_high = []  Low confidence list: D_low = []  Processed crops: C_processed = [] 2. Initial detection:  D_all = M.detect(I, T1)
3. Split detections: FOR d in D_all: IF d.confidence \(\geq\) T2:  D_high.append(d) ELSE IF d.area \(\leq\) 5% of Larea:  D_low.append(d)
4. Process low confidence detections: FOR d in D_low:  Expand d to Crop_box by 8x.
```  
**Algorithm 1**Pseudo code for CR process * Initialization and Initial Detection  
The algorithm begins by performing object detection on the input image \(I\) am using the YOLO model M. During the initial detection phase, the confidence threshold \(T_{2}\) is set at 0.1 or higher to maximize object detection coverage. All detected results are stored in \(D_{all}\) and divided based on confidence levels and area conditions.  
* Separation of Results  
The initially detected objects are categorized into two groups based on a secondary confidence threshold \(T_{2}\) (set at 0.5) and the area conditions of the detected object:  
High Confidence Group (\(D_{high}\)): includes objects with confidence scores greater than or equal to \(T_{2}\) (0.5), a typical threshold for object detection models.  
Figure 4: Visual representation and algorithm of the proposed CR process.  
Low Confidence Group (\(D_{low}\)): comprises objects with confidence scores below \(T_{2}\) (0.5) and bounding box areas less than 5% of the total image area.  
This separation process identifies small objects with low confidence as additional targets for detection.  
* Handling Low Confidence Object  
For each object in the low confidence group (\(D_{low}\)), a crop area is created by expanding its bounding box by a factor of 8. If the extended crop area overlaps with a previously processed region (\(C_{processed}\)), the redetection of the corresponding box is skipped to avoid redundant processing. Non-overlapping crop areas are subjected to redetection using the YOLO model \(M\). The resulting redetected objects (\(D_{crop}\)) are then added to the high confidence group (\(D_{high}\)) after transforming their coordinates to the original image scale.
* Merge and Visualizing Final Results  
The final detection result (\(D_{final}\)) are generated by merging the high confidence group (\(D_{high}\)) with the redetection results (\(D_{crop}\)). These results are visualized in the image using bounding boxes and class labels. The final processed image is subsequently stored for further analysis.  
Through this, low reliability appeared due to the blur or low IoU of a small object in the image, so the detection rate could be increased by detecting the dropped part from the original image once more through the CR process