Object Detection using yolov8
In the world of computer vision, YOLOv8 object detection really stands out for its super accuracy and speed. It’s the latest version of the YOLO series, and it’s known for being able to detect objects in real-time. YOLOv8 takes web applications, APIs, and image analysis to the next level with its top-notch object detection. In this article, we will see how yolov8 is utilised for object detection.
Overview of YOLO
YOLO (You Only Look Once) is a game-changing object detection algorithm that came on the scene in 2015, known for its lightning-fast processing of entire images at once. YOLOv8 is the newest version, taking previous iterations and making them even speedier and more accurate. The YOLO evolution includes versions like YOLOv1, v2, v3, v4, and v5, each bringing improvements like real-time processing, batch normalization, and better detection accuracy. YOLOv8 brings in cutting-edge techniques to take object detection performance even further.
Key Features of yolov8:
YOLOv8 has brought in some key features that set it apart from earlier versions:
- Anchor-Free Architecture: Instead of the traditional anchor-based detection, YOLOv8 goes for an anchor-free approach. This change makes training simpler and helps the model work well with different datasets.
- Advanced Data Augmentation: By using techniques like MixUp and Mosaic, YOLOv8 toughens up the model and helps it work well in real-world applications. Mixing images in training provides diverse examples, boosting the model’s accuracy and reliability.
- Adaptive Training: This feature lets YOLOv8 adjust the learning rate dynamically and balance the loss function more effectively during training, leading to optimized performance and higher detection accuracy.
- Self-Attention Mechanism: YOLOv8 brings in a self-attention mechanism, which helps the model understand the relationships and dependencies between different features in an image. This is especially handy for complex scenes where understanding context is key.
- Improved Backbone and Neck Architectures: The model uses state-of-the-art architectures for feature extraction, which are crucial for accurate object detection. These improvements help the model handle a wide range of object detection tasks efficiently.
- Efficiency and Speed: Despite all these advancements, YOLOv8 strikes a balance between accuracy and speed, making it perfect for real-time object detection applications.
YOLOv8’s development is a major milestone in the world of computer vision, especially for object detection tasks. Its strong architecture and innovative features ensure that it remains a top choice for developers and researchers looking to implement efficient and accurate object detection in their applications.
Prerequisites:
We need to install packages and set up the environment to implement object detection using yolov8:
pip install ultralytics
For Conda users: conda install -c conda-forge ultralytics
Using docker: sudo docker pull ultralytics/ultralytics
And to set up the environment:
- Install Python: Ensure Python is installed on your system.
- Create a Virtual Environment: Use python -m venv yolov8-env in your terminal to create a virtual environment.
- Activate Virtual Environment:
Unix/macOS: source yolov8-env/bin/activate
Windows: .\yolov8-env\Scripts\activate
YOLOv8 also lets you use a Command Line Interface (CLI) to easily train models and run detections without needing to write Python code. It’s great for those who like using commands directly.
To download the video we are using in this video: click here.
Implementing YOLOv8 for Object Detection
When it comes to spotting and tallying up vehicles, here’s how we do it in three simple steps:
Step 1: Importing Necessary Libraries
All the necessary that will be used for our model are imported.
import ultralytics
import supervision
import torch
import cv2
from collections import defaultdict
import supervision as sv
from ultralytics import YOLO
Step 2: Loading the pretrained model
By using this code we load the YOLOv8 (You Only Look Once version 8) model from the ultralytics library to perform object detection on a video file (d.mp4).
Here is a detailed explanation of each step and argument in the code:
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Run inference on 'bus.jpg' with arguments
model.predict(source="d.mp4", save=True, imgsz=320, conf=0.5)
Step 3: Tracking the Model
This code use the YOLOv8 model to include object tracking on a video file (d.mp4). Here’s a detailed explanation of each step and the parameters used in the track method:
# Configure the tracking parameters and run the tracker
model = YOLO('yolov8n.pt')
results = model.track(source="d.mp4",conf=0.3, iou=0.5, save=True, tracker="bytetrack.yaml")
Step 4: Line Crossing Detection in Video using ByteTrack
The code loads a YOLOv8 model to track objects in a video (d.mp4) and detects when they cross a defined line. It captures and processes each frame, annotating tracked objects and counting those that cross the line. The annotated video with crossing counts is saved as output_single_line.mp4.
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
# Set up video capture
cap = cv2.VideoCapture("d.mp4")
# Define the line coordinates
START = sv.Point(182, 254)
END = sv.Point(462, 254)
# Store the track history
track_history = defaultdict(lambda: [])
# Create a dictionary to keep track of objects that have crossed the line
crossed_objects = {}
# Open a video sink for the output video
video_info = sv.VideoInfo.from_video_path("d.mp4")
with sv.VideoSink("output_single_line.mp4", video_info) as sink:
while cap.isOpened():
success, frame = cap.read()
if success:
# Run YOLOv8 tracking on the frame, persisting tracks between frames
results = model.track(frame, classes=[2, 3, 5, 7], persist=True, save=True, tracker="bytetrack.yaml")
# Get the boxes and track IDs
boxes = results[0].boxes.xywh.cpu()
track_ids = results[0].boxes.id.int().cpu().tolist()
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Plot the tracks and count objects crossing the line
for box, track_id in zip(boxes, track_ids):
x, y, w, h = box
track = track_history[track_id]
track.append((float(x), float(y))) # x, y center point
if len(track) > 30: # retain 30 tracks for 30 frames
track.pop(0)
# Check if the object crosses the line
if START.x < x < END.x and abs(y - START.y) < 5: # Assuming objects cross horizontally
if track_id not in crossed_objects:
crossed_objects[track_id] = True
# Annotate the object as it crosses the line
cv2.rectangle(annotated_frame, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 255, 0), 2)
# Draw the line on the frame
cv2.line(annotated_frame, (START.x, START.y), (END.x, END.y), (0, 255, 0), 2)
# Write the count of objects on each frame
count_text = f"Objects crossed: {len(crossed_objects)}"
cv2.putText(annotated_frame, count_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Write the frame with annotations to the output video
sink.write_frame(annotated_frame)
else:
break
# Release the video capture
cap.release()
Output:
The output is a mp4 file which will be stored in your environment, the image below represents how line crossing detection is implemented in the entire video.
Output
In this article, we dove into advances of YOLOv8 in object detection. We talked about how it’s super speedy, accurate, and versatile. YOLOv8 is a big deal in computer vision, opening up new possibilities for research and development. Its impact on stuff like autonomous vehicles and surveillance is huge, and there’s tons of potential for more innovation and exploration in the field.
Object Detection using yolov8 – FAQs
What are the known limitations of YOLOv8?
YOLOv8 might not be the ideal option for tasks that need really precise instance segmentation. Plus, it needs a lot of computational resources for training and has a larger model size compared to some other models.
What steps should I take to enhance the accuracy of YOLOv8?
For better accuracy with YOLOv8, start by getting a well-labeled dataset. Use the Ultralytics API to kick off the YOLOv8 model, then train the model using this dataset while adjusting hyperparameters. Finally, test the model’s performance to ensure it’s more accurate.
How to boost the performance of YOLOv8?
To boost YOLOv8’s performance, begin with the default settings to set a performance baseline. Make sure your dataset is comprehensive and accurately labeled. Adjust important performance parameters like image size and batch size as necessary.