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Yolov8s Nlf Head Detection
An object detection model based on YOLOv8s, specifically designed to detect helmets and head positions in American football games.
Downloads 202
Release Time : 1/29/2023
Model Overview
This model uses the YOLOv8s architecture, optimized for helmet and head detection in American football games, capable of recognizing helmets in various states (e.g., blurry, difficult, partially visible, etc.).
Model Features
Multi-category Helmet Detection
Capable of recognizing five different helmet states (normal, blurry, difficult, partially visible, and sideline helmets).
Optimized Based on YOLOv8s
Utilizes the lightweight YOLOv8s architecture, maintaining high accuracy while offering fast inference speeds.
Specialized for Sports Scenarios
Specifically optimized for American football game scenarios, adapting to complex field environments and fast-moving targets.
Model Capabilities
Object detection in images
Multi-category object recognition
Sports game video analysis
Use Cases
Sports Analysis
Game Video Analysis
Automatically detects helmet positions of players in game videos for tactical analysis and player tracking.
Generates player position heatmaps and movement trajectories
Safety Monitoring
Monitors helmet status to assist in identifying potential head collision risks.
Identifies abnormal helmet states (e.g., dislodged or damaged)
Broadcast Enhancement
Automatic View Switching
Automatically selects the best camera angle based on helmet detection results.
Enhances the viewing experience of game broadcasts
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