Car Object Detection Using YOLOv8

In this project, I aim to implement a car object detection algorithm using the YOLO (You Only Look Once) architecture, known for its real-time speed and high accuracy. YOLO processes images in a single pass through the network, enabling it to detect objects quickly and efficiently. I will use a dataset containing images of cars from various angles to build and fine-tune the detection model. The goal is to achieve accurate detection of cars in real-world scenarios using this state-of-the-art algorithm.

What is YOLOv8 ?

YOLOv8 is a state-of-the-art computer vision model architecture developed by Ultralytics, the creators of YOLOv5. It is designed for real-time object detection and offers significant improvements in accuracy and speed over previous YOLO versions.  

Key Features and Improvements:

• Advanced Architecture: YOLOv8 incorporates a new backbone architecture, a redesigned neck, and improved head designs for enhanced performance.  

• Anchor-Free Detection: It adopts an anchor-free approach, eliminating the need for predefined anchor boxes, which improves generalization and learning speed.  

• Efficient Training and Inference: YOLOv8 is optimized for efficient training and inference, making it suitable for deployment on various devices, including NVIDIA Jetson, NVIDIA GPUs, and macOS systems with Roboflow Inference.
   

• Versatile Applications: It can be used for a wide range of object detection tasks, including image and video analysis, surveillance systems, autonomous vehicles, and more.  

How YOLOv8 Differs from Previous YOLO Versions:

• Enhanced Performance: YOLOv8 achieves higher accuracy and faster inference speeds compared to YOLOv5 and other previous versions.

• Anchor-Free Approach: The shift to anchor-free detection simplifies the training process and improves model generalization.  

• New Architecture: The redesigned backbone, neck, and head architectures contribute to the improved performance.  

• Wider Range of Applications: YOLOv8 can be applied to a broader range of object detection tasks due to its versatility.  

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Let`s Start

1- Import Necessary Libraries

!pip install ultralytics

import os  # For interacting with the operating system (e.g., file paths)
import pandas as pd  # For loading and manipulating CSV data (bounding boxes and image info)
import numpy as np  # For numerical operations and array handling
import cv2  # For image processing and manipulation (OpenCV library)
import matplotlib.pyplot as plt  # For visualizing data and images (plots)
import seaborn as sns  # For advanced data visualization (especially histograms and distributions)
from glob import glob  # For finding all image files in a directory (using wildcard patterns)
from sklearn.model_selection import train_test_split  # For splitting the dataset into training and testing sets
from PIL import Image
from ultralytics import YOLO


import warnings
warnings.filterwarnings("ignore", "use_inf_as_na option is deprecated")

2- Exploratory Data Analysis (EDA)

# Create necessary directories
!mkdir -p "/kaggle/working/data"
!mkdir -p "/kaggle/working/data/images"
!mkdir -p "/kaggle/working/data/images/train"
!mkdir -p "/kaggle/working/data/images/val"
!mkdir -p "/kaggle/working/data/labels"
!mkdir -p "/kaggle/working/data/labels/train"
!mkdir -p "/kaggle/working/data/labels/val"

root_dir = "/kaggle/working/data"
labels_dir = "/kaggle/working/data/labels"
images_dir = "/kaggle/working/data/images"

# Define paths
train_data = "/kaggle/input/car-object-detection/data/training_images"
csv_data = "/kaggle/input/car-object-detection/data/train_solution_bounding_boxes (1).csv"
test_data = "/kaggle/input/car-object-detection/data/testing_images"

# Loading the CSV data
df = pd.read_csv(csv_data)

# Display the first few rows of the dataframe to understand its structure
df.head()

print(df.info())

# Distribution of bounding box sizes
plt.figure(figsize=(12, 6))
df['width'] = df['xmax'] - df['xmin']
df['height'] = df['ymax'] - df['ymin']
sns.scatterplot(x='width', y='height', data=df)
plt.title('Distribution of bounding box sizes')
plt.xlabel('Width')
plt.ylabel('Height')
plt.show()

# Visualizing the distribution of bounding box widths and heights
df['box_width'] = df['xmax'] - df['xmin']
df['box_height'] = df['ymax'] - df['ymin']

plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
sns.histplot(df['box_width'], bins=20, kde=True)
plt.title('Distribution of Bounding Box Widths')

plt.subplot(1, 2, 2)
sns.histplot(df['box_height'], bins=20, kde=True)
plt.title('Distribution of Bounding Box Heights')

plt.tight_layout();
plt.show();

3- Preparing the Dataset for YOLO

# Prepare YOLO format annotations
def create_yolo_annotation(row, img_width, img_height):
    x_center = ((row['xmin'] + row['xmax']) / 2) / img_width
    y_center = ((row['ymin'] + row['ymax']) / 2) / img_height
    width = (row['xmax'] - row['xmin']) / img_width
    height = (row['ymax'] - row['ymin']) / img_height
    return f"0 {x_center} {y_center} {width} {height}"
# Create YOLO annotations and copy images
for img_name in df['image'].unique():
    img_df = df[df['image'] == img_name]
    img_path = os.path.join(train_data, img_name)
    img = cv2.imread(img_path)
    if img is not None:
        img_height, img_width = img.shape[:2]
        
        # Decide whether to put in train or val folder
        if np.random.rand() < 0.8:  # 80% train, 20% val
            subset = "train"
        else:
            subset = "val"
        
        # Copy image
        dst_img_path = os.path.join(images_dir, subset, img_name)
        cv2.imwrite(dst_img_path, img)
        
        # Create annotation file
        annotation_path = os.path.join(labels_dir, subset, f"{img_name.split('.')[0]}.txt")
        with open(annotation_path, 'w') as f:
            for _, row in img_df.iterrows():
                yolo_annotation = create_yolo_annotation(row, img_width, img_height)
                f.write(yolo_annotation + '\n')
# Create YAML configuration file
yaml_content = f"""
path: {root_dir}
train: images/train
val: images/val

nc: 1
names: ['car']
"""

with open('car_detection.yaml', 'w') as f:
    f.write(yaml_content)

print("YAML configuration file created.")

4- Training the YOLO Model

model = YOLO('yolov8n.pt')

# Disable W&B logging to avoid the API key prompt
os.environ["WANDB_MODE"] = "disabled"
results = model.train(
    data='car_detection.yaml',
    epochs=30,
    imgsz=640,
    batch=16,
    name='car_detection_model'
)

5- Save The Model

model.save('car_detection_model.pt')
print("Model saved successfully.")

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The End