# YOLOv8 with Post-Processing for Small Object Detection Enhancement
## 1 Introduction  
Research on convolutional neural network (CNN)-based image classification and object detection models has advanced significantly in recent years. Notably, the development of object detection models, such as You Only Look Once (YOLO) [1, 2], has progressed rapidly, continuously releasing new versions. However, recent trends in computer vision research indicate a decline in studies focused on CNN-based object detection [3, 4], while the popularity of image models like transformers and generative models is increasing. This shift suggests that the exploration of new CNN-based approaches has plateaued, given the substantial improvements and high accuracy already achieved in object detection and classification models [5], which are key components of unstructured big-data analysis pipelines. While promising models such as YOLOv8 and EfficientDet have emerged [6], challenges remain in detecting small objects in images. Although these models have madesignificant strides in detecting smaller objects compared to earlier versions, they still struggle with detecting distant or tiny objects [7; 8; 9]. The detection of small objects presents unique challenges compared to larger objects. These objects are more vulnerable to low resolution and are prone to background noise, occlusion, and other disturbances. Due to their limited size, small objects often lose crucial details during feature extraction [10; 11]. This issue is exacerbated in deep convolutional layers of object detection models, making accurate detection even more challenging. Furthermore, overlapping object boundaries and complex environments complicate the detection of small objects [12; 13]. Traditional methods for small object detection, such as increasing the input size or utilizing data augmentation, do not effectively address these challenges. Deep learning-based approaches, including two-stage detectors, like Faster Region-based Convolutional Neural Network (Faster R-CNN) [14] features, and single-stage detectors, like single-shot multi-box detectors (SSD) [15] and YOLO, offer higher accuracy and efficiency but still have limitations when it comes to detecting small objects. Consequently, research continues to explore innovative solutions, such as high-resolution processing and multi-scale feature extraction, to improve performance. One fundamental approach to tackling this issue is using higher-resolution inputs during inference [16; 17; 18; 19]. For example, YOLOv8 typically uses an input image resolution of 640 \(\times\) 640 pixels, but it can be increased to 1280 \(\times\) 1280 pixels to improve small object detection. However, such methods are limited by the increased computational cost. Recent progress can be broadly divided into two independent strands. The first revisits tentative detections by repeating inference in a cascaded manner, thereby refining localization accuracy [20; 21; 22]. The second strand replaces conventional up-sampling operators with content-aware modules--such as Content-Aware ReAssemble of FEatures (CARAFE) or deformable convolutions--to reinforce multi-scale feature fusion [22; 23]. However, prior studies have pursued either re-detection strategies or neck-level feature reconstruction in isolation. While several studies have examined these two directions separately, the potential benefits of uniting confidence-guided re-detection with content-aware feature reconstruction within a single YOLOv8 framework have yet to be systematically investigated. This paper proposes the pipeline depicted in Figure 1, which integrates an additional image post-processing step with an enhanced YOLOv8n model--a lightweight YOLO family variant--to address existing methods' computational cost issues and produce more effective results. The key idea is as follows: Object detection models' confidence score threshold for reliable bounding box detection is typically 50%. However, as object size decreases, the model's reliability diminishes due to feature loss and reduced Intersection over Union (IoU) values.