Human Head Detection System

About the Project
This project focuses on the tasks of crowd counting and safety monitoring through accurate detection of human heads. This is a fundamental component of modern video surveillance systems, where it is crucial to determine the number of people even in dense crowds.

Training Pipeline
• Model: YOLOv11n (the fastest version, optimized for edge devices)
• Epochs: 100
• Device: GPU (CUDA)
• Result: Consistently high accuracy (mAP50 ≈ 0.97)

Visualization
A custom OpenCV-based function renders bounding boxes and displays the model’s confidence score above each detected object.

Training Results
The model demonstrates strong generalization capabilities. Loss function graphs show stable convergence.
The mAP50 (Mean Average Precision @ 50% IoU) score exceeds 0.9, which is considered an excellent result.
The mAP50-95 metric reaches 0.65+, which is a strong performance for lightweight detectors.

Tech Stack
• Core: Python
• ML: Ultralytics YOLOv11, PyTorch
• CV: OpenCV

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