Detect cars, trucks, buses, and motorcycles in videos
Analyze video for object detection and counting
A UI for drone detection for YOLO-powered detection system.
Powerful foundation model for zero-shot object tracking
Detect objects in images or videos
Detect objects in live video feeds
Analyze images and videos to identify objects
Process videos to detect and track objects
Track people in a video and capture faces
Generate annotated video with object detection
Process video to count and track cars
Dino-X-API-Demo::Alteredverse
Track and count vehicles in real-time
Vehicle Detection Using YOLOv8 is a state-of-the-art object detection system designed to identify and track vehicles such as cars, trucks, buses, and motorcycles in video footage. Built on the YOLOv8 framework, it leverages advanced deep learning algorithms to achieve high accuracy and real-time performance. This tool is particularly useful for surveillance, traffic monitoring, and autonomous systems, enabling the detection of vehicles with precision and efficiency.
What hardware is required to run Vehicle Detection Using YOLOv8?
Vehicle Detection Using YOLOv8 can run on a variety of hardware, including CPUs, GPUs, and TPUs. For optimal performance, a GPU with CUDA support is recommended.
Can the model detect vehicles in low-light conditions?
Yes, YOLOv8 can detect vehicles in low-light conditions, but accuracy may vary depending on the quality of the input video.
How do I improve detection accuracy?
To improve detection accuracy, ensure the video input has a high resolution and clear visibility. Additionally, fine-tuning the model for your specific use case can enhance performance.