YOLOv8 Model Architecture Analysis: Network Hierarchy and Feature Extraction Principles

# 1. Overview of the YOLOv8 Model

YOLOv8 is a real-time object detection model developed by Megvii Research Institute, released in April 2022. It achieved a 61.7% mAP on the COCO dataset, surpassing all mainstream object detection models at the time and becoming the most advanced real-time object detection model.

YOLOv8 adopts a novel network structure known as CSPDarknet53, which is more lightweight and efficient than the previous version. Additionally, YOLOv8 introduces new neck and detection head networks, further enhancing the model's performance.

# 2. YOLOv8 Network Hierarchy

The YOLOv8 network features a typical encoder-decoder structure, consisting of three parts: the backbone network, the neck network, and the detection head.

### 2.1 Backbone Network

The backbone network is responsible for extracting features from the input image. YOLOv8 provides two backbone network options: CSPDarknet53 and CSPDarknetX.

#### 2.1.1 CSPDarknet53

CSPDarknet53 is the default backbone network for YOLOv8, based on the Darknet53 network. CSPDarknet53 employs a Cross-Stage Partial Network (CSP) structure, dividing the network into multiple stages, each consisting of several convolutional layers. Within each stage, a portion of features is directly passed to the next stage, while another portion is passed through residual connections. This structure enhances the network's feature extraction capability while reducing computational costs.

#### 2.1.2 CSPDarknetX

CSPDarknetX is an extended version of CSPDarknet53, featuring additional convolutional layers and CSP modules. CSPDarknetX offers stronger feature extraction capability but at a higher computational cost.

### 2.2 Neck Network

The neck network is responsible for fusing the features extracted by the backbone network into feature maps suitable for detection tasks. YOLOv8 provides two options for the neck network: Spatial Pyramid Pooling (SPP) and Path Aggregation Network (PAN).

#### 2.2.1 Spatial Pyramid Pooling

SPP is a classic feature fusion method that divides the input feature map into multiple grids and performs max pooling on each grid. SPP can extract features at different scales, enhancing the robustness of detection tasks.

#### 2.2.2 Path Aggregation Network

PAN is an advanced feature fusion method that aggregates feature maps from different stages of the backbone network. PAN employs top-down and bottom-up paths to fuse feature maps at different scales, resulting in a richer feature representation.

### 2.3 Detection Head

The detection head is responsible for converting the fused feature maps into detection results. YOLOv8 provides two options for the detection head: YOLOv3 and YOLOv4 detection heads.

#### 2.3.1 YOLOv3 Detection Head

The YOLOv3 detection head employs a 3x3 convolutional layer and a fully connected layer. The 3x3 convolutional layer extracts features, while the fully connected layer predicts bounding boxes and class probabilities.

#### 2.3.2 YOLOv4 Detection Head

The YOLOv4 detection head introduces two innovations based on YOLOv3:

- **SPP Module:** The SPP module fuses feature maps at different scales, enhancing the robustness of detection tasks.
- **Mish Activation Function:** The Mish activation function is a smooth nonlinear activation function that can improve the convergence speed and accuracy of the network.

# 3.1 Convolutional Neural Networks

A Convolutional Neural Network (CNN) is a type of deep learning model particularly suited for processing data with a grid-like structure, ***Ns extract features from input data using convolutional and pooling operations.

#### 3.1.1 Convolutional Operation

The convolutional operation is a fundamental component of CN