# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 3 Methodology
### Deep Learning-Based Object Detection  
Deep learning-based object detection algorithms are divided into R-CNN and YOLO model series. The R-CNN series is excellent in object detection with a high rate of accuracy but is disadvantaged due to its' detection speed being slower than the YOLO series [11]. Therefore, there is a limit to its application in detecting objects, for example parcels that move at high speed in real time in actual logistics sites. In this context, the YOLO algorithm series was developed to quickly learn the generalized characteristics of an object and solve the speed problem using the concept of regression. The YOLO Algorithm stands for 'You Only Look Once', and YOLO's various versions of models accurately and quickly classify the location and classification of detection targets using one-stage neural networks [12]. In 2015, [PERSON] et al. introduced the first version of YOLO [13], and in the past few years, several successor versions, such as yolov2, v3, v5, and v6 with improved performance in terms of computational speed and object detection have been announced [14; 15; 16].  
YOLO has a simple structure that can directly output the location and category of the boundary box through the neural network as shown in the Figure 2[17]. The YOLO architecture consists of 24 convolutional layers and two fully connected layers. YOLO estimates several bounding boxes per grid cell, but the bounding boxes which have the closest IoU (Intersection over Union) with the Ground Truth (GT) are selected. It is called non-maximum suppression [18].  
The original YOLO model has two disadvantages. The first is inaccurate location estimation, and the second is the low recall rate. Therefore, YOLOv2 presented an improved upgrade version in these two aspects. YOLOv2 does not deepen or widen the network but instead simplifies the algorithm to improve performance and speed [14]. Additionally, compared to YOLOv2, YOLOv3 is characterized by using multi-scale features for object detection and coordinating the original network structure. The feature extraction network of YOLOv3 used the residual model. The architecture of YOLOv3 contained 53 convolution layers, so it was called Darknet-53, compared with Darknet19 used by YOLOv2 [15]. The YOLOv4 algorithm has a great change when compared with previous models; it focuses on comparing data and significantly improves speed and performance [19]. YOLO v4 and v5 were proposed by developers other than the original YOLO developer.  
YOLOv5 has features that can be applied to iOS using PyTorch. YOLOv5 may be relatively low in terms of accuracy compared to other models, but it shows an overwhelming performance in terms of speed. However, unlike existing models, YOLOv5 does not have published papers or technical reports. YOLOv5 is the latest enhancement that incorporates several new technologies that have already been proven in other networks [20]. Depending on the width and depth of the model, YOLOv5 can be divided into four versions: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x.  
Figure 2: Architecture of YOLO Algorithm.  
In the case of YOLOv6, while having a deeper network structure, it started detecting objects of more diverse sizes by increasing them from the previous three scales to four. It is characterized by having a much faster speed than the last v5 model [3]. The performance, speed, and analysis settings of YOLO algorithms are summarized in Table 2. Among various YOLO algorithms, YOLOv5, is the most efficient in terms of speed and performance and is being applied in several recent research papers, is used for this analysis to conduct research [11; 21].