## 2 Related Works
### Development of YOLO Versions  
In 2015, YOLO's first version (YOLOv1) [61] utilized a single neural network to predict many classes' bounding box coordinates and confidence scores. YOLOv1 transforms object recognition into challenges with regression. YOLOv1's primary objective is to build a CNN network capable of predicting tensors. On Pascal VOC2007, YOLOv1 analyzes images at 45 frames per second (fps) rate, which is twice to nine times quicker than Faster R-CNN. Version 2 of YOLO (YOLOv2, or YOLO9000) [62] is a simplified version of YOLO's single-stage real-time object detection model. It preserves the YOLOv1's speed advantage while aiming to boost the mAP value. It improves on YOLOv1 in several ways, including the use of Darknet-19 as batch normalization, backbone, a high-resolution classifier, and anchor boxes to predict bounding boxes. It cannot yet generalize to novel or unexpected aspect ratios or configurations of objects. In addition, YOLO's loss function treats errors consistently in small and large bounding boxes and is not perfect. Then, a new version of YOLOv2 designated YOLOv3 [63] was created using logistic regression to predict the objectiveness score of each bounding box and modify how cost functions are formed. YOLOv3 calculates each label's binary cross-entropy loss. This may result in a reduction in computing complexity by omitting SoftMax features. [64] proved that YOLOv2 outperforms Faster R-CNN in the identification of fashion clothes. By combining the previous research, it was determined that the YOLO method is more suited for detecting fashion items than the two-stage detection techniques. YOLOv3 is three times quicker than the previous version but has a more significant positioning error. However, it significantly improves tiny object detection. In 2020, YOLOv4 [65] was released, built on the Darknet framework, a freely available neural network framework written in C and CUDA. YOLOv4 takes advantage of the PANet (Path Aggregation Network). This distinction enables the YOLOv4 to be quicker and more precise than the YOLOv3.  
Researchers are continually trying to enhance the YOLO standard algorithm for one-stage processes that are as efficient at identifying the position of objects as two-stage approaches. In 2021, ref. [66] proposed the YOLO method with a larger effective receptive field (ERF-YOLO), which would reduce the number of model parameters while maintaining the same performance. BAFPN [42] is a novel bidirectional Feature Pyramid Network that constructs precise object recognition networks using YOLOv4 and Adaptively Spatial Feature Fusion. Utilizing the Exponential Moving Average improves network performance. The constructed network maintains a high computing speed and improves the mAP. Ref. [67] proposed that the YOLO-V4 backbone network CSPDarknet53 be integrated with DenseNet. To accommodate little cherries, the YOLO-V4 model's a priori box is modified to resemble a cherry-shaped circular marking box. In our study, we introduced the new FC-YOLOv4 model, which is a tangible example of training a network with a small dataset while maintaining detection speed and weight to apply computer vision to the real world. It has the potential to be used in future fashion image analysis research.