Vehicle Detection Using YOLOv8

What is Vehicle Detection Using YOLOv8 ?

Vehicle Detection Using YOLOv8 is a real-time object detection system designed to identify and classify vehicles in video streams or images. It leverages the YOLOv8 model, a state-of-the-art convolutional neural network (CNN) known for its high accuracy and speed in detecting objects. This application is specifically optimized for detecting cars, trucks, buses, and motorcycles, making it ideal for applications such as traffic monitoring, surveillance, and autonomous systems.

Features

• High Accuracy: Detects vehicles with high precision even in complex or crowded scenes.
• Real-Time Detection: Processes video frames quickly, making it suitable for real-time applications.
• Multiple Vehicle Types: Capable of identifying cars, trucks, buses, and motorcycles.
• Support for Various Video Sources: Works with live camera feeds, pre-recorded videos, or stored images.
• Customizable: Allows users to adjust detection parameters such as confidence thresholds.
• Low Resource Requirements: Optimized to run efficiently on CPUs and GPUs.

How to use Vehicle Detection Using YOLOv8 ?

1. Install the Application: Download and install the Vehicle Detection Using YOLOv8 tool.
2. Download the YOLOv8 Model: Ensure the YOLOv8 model weights are downloaded for vehicle detection.
3. Load the Video or Image: Input the video file, image, or camera feed for processing.
4. Run the Detection: Execute the detection script to analyze the input and identify vehicles.
5. View Results: The system will display the video/image with bounding boxes and labels for detected vehicles.

Frequently Asked Questions

1. What types of vehicles can this system detect?
This system is trained to detect cars, trucks, buses, and motorcycles.
2. Can I use this for live camera feeds?
Yes, the system supports live camera feeds and is optimized for real-time detection.
3. How do I improve detection accuracy?
You can improve accuracy by adjusting the confidence threshold to a higher value or using a more powerful GPU for faster processing.