YOLOv8 Practical Case: Road Traffic Vehicle Detection and Counting

#YOLOv8 Real-world Case Study: Road Traffic Vehicle Detection and Counting

## 1.1 Introduction to YOLOv8 Model

YOLOv8 is one of the most advanced real-time object detection models, ***pared to its predecessor YOLOv7, YOLOv8 has significantly improved in accuracy and speed. YOLOv8 adopts a new network architecture known as Cross-Stage Partial Connections (CSP), which reduces the amount of computation while maintaining the model's accuracy. Additionally, YOLOv8 employs a new training strategy called SimOTA, which enhances the model's performance in detecting small objects.

## 2. Setting Up the YOLOv8 Practical Environment and Data Preparation

### 2.1 Environment Setup and Dependency Installation

**Environment Requirements:**

* Operating System: Ubuntu 18.04 or higher versions
* Python: 3.7 or higher versions
* CUDA: 11.1 or higher versions
* cuDNN: 8.0 or higher versions
* PyTorch: 1.8 or higher versions

**Dependency Installation:**

1. Install CUDA and cuDNN, referring to the official documentation for specific steps.
2. Create a virtual environment and install PyTorch:

python3 -m venv venv
source venv/bin/activate
pip install torch torchvision

3. Install YOLOv8:

git clone ***
***
***

### 2.2 Dataset Acquisition and Preprocessing

**Dataset Acquisition:**

* COCO dataset: Contains 80 object categories, used for training general-purpose object detection models.
* VOC dataset: Contains 20 object categories, used for training models in specific domains.
* Custom dataset: Collect images and annotations that meet the specific task requirements.

**Dataset Preprocessing:**

1. **Image preprocessing:** Resize images to a uniform size and perform normalization.
2. **Annotation preprocessing:** Convert annotation files to a format supported by YOLOv8, including class IDs, bounding box coordinates, and confidence scores.
3. **Dataset splitting:** Divide the dataset into training, validation, and test sets, typically in a ratio of 7:2:1.

**Code Example:**

import os
from PIL import Image
import numpy as np

# Image preprocessing
def preprocess_image(image_path, input_size):
    image = Image.open(image_path)
    image = image.resize((input_size, input_size))
    image = np.array(image) / 255.0
    return image

# Annotation preprocessing
def preprocess_label(label_path, input_size):
    with open(label_path, "r") as f:
        labels = f.readlines()
    labels = [label.strip().split(" ") for label in labels]
    for label in labels:
        label[1:] = [float(x) for x in label[1:]]
        label[1:] = label[1:] * input_size
    return labels

# Dataset splitting
def split_dataset(dataset_path, train_ratio=0.7, val_ratio=0.2):
    images = os.listdir(dataset_path)
    np.random.shuffle(images)
    train_size = int(len(images) * train_ratio)
    val_size = int(len(images) * val_ratio)
    train_images = images[:train_size]
    val_images = images[train_size:train_size+val_size]
    test_images = images[train_size+val_size:]
    return train_images, val_images, test_images

## 3. YOLOv8 Model Training and Evaluation

### 3.1 Training Configuration and Parameter Tuning

#### Training Configuration

The YOLOv8 model training configuration mainly includes the following parameters:

| Parameter | Description |
|---|---|
| batch_size | Training batch size |
| epochs | Number of training epochs |
| learning_rate | Learning rate |
| momentum | Momentum |
| weight_decay | Weight decay |
| warmup_epochs | Warm-up epochs |
| warmup_lr_multiplier | Warm-up learning rate multiplier |
| optimizer | Optimizer |
| loss | Loss function |
| lr_scheduler | Learning rate scheduler |

#### Parameter Tuning

Training configuration parameter tuning is a key step in improving model performance. Here are some common parameter tuning tips:

- **Batch size:** The batch size affects the training speed and model performance. Generally, a larger batch size can increase training speed but may lead to overfitting.
- **Learning rate:** The learning rate controls the speed of model parameter updates. A higher learning rate can accelerate training but may cause instability in the model.
- **Momentum:** Momentum can smooth gradient updates and help prevent the model from getting stuck in local optima.
- **Weight decay:** Weight decay can prevent overfitting but may reduce model performance.
- **Warm-up phase:** The warm-up phase gradually increases the learning rate, which helps with model stability.

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