The Safety Cone Detection model is designed to address the critical need for real-time detection of safety cones on roads and construction sites. Safety cones play a pivotal role in ensuring public safety by marking hazardous areas, construction zones, or redirecting traffic. Accurate and swift detection of these cones is essential to prevent accidents and streamline traffic flow.
Utilizes the YOLOv8 model for high accuracy and real-time performance. Offers fast frame-per-second (FPS) rates, ensuring swift detection and response. Provides robust detection capabilities, identifying safety cones across various environments.
In addition to the standard YOLOv8 model, we offer optimized versions in both .onnx and .blob formats. These optimized models are tailored for efficient deployment on a wide range of devices, including the OAK-D camera. They are streamlined for optimal performance and resource utilization.
Users can effortlessly deploy the Safety Cone Detection model using the command-line interface (CLI). Here's an example of usage:
yolo predict model=Cone.pt source='path-to-video'
pip install ultralytics
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