Detect cars, trucks, buses, and motorcycles in videos
Process video to detect and highlight objects
Process videos to detect and track objects
Detect objects in images or videos
Detect objects in images, videos, and live webcam streams
Model Yolo
Analyze images and videos to identify objects
yolo-bdd-inference
Control object motion in videos using 2D trajectories
Detect and track objects in images or videos
用于学习，验证识别效果
Identify objects in images and videos
Detect objects in real-time from webcam video
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.
• 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.
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.