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Detection System

This is where the trained AI model is put to work. The “Inference Engine” is the bridge between the static .pt model file and the live webcam feed.

The Detection Lifecycle

The system runs in a continuous loop. Each iteration must be fast enough to maintain a smooth frame rate (FPS) so that no drowsiness events are missed.

Sequence Diagram: Frame-by-Frame Processing

sequenceDiagram
    participant Cam as Webcam
    participant AI as YOLOv11 Model
    participant Engine as Detection Engine
    participant History as Temporal Window (Deque)
    participant Alert as Alert System

    Cam->>Engine: Capture Raw Frame
    Engine->>AI: Pass Frame for Inference
    AI-->>Engine: Prediction (Open/Closed + Confidence)
    Engine->>Engine: Apply Confidence Cutoff
    Engine->>History: Push State (True/False)
    History->>History: Pop Oldest Frame (Maintain Size 15)
    Engine->>History: Calculate Closed Ratio
    Engine->>History: Check Consecutive Streak
    alt Drowsy State Detected
        Engine->>Alert: Trigger Alert Sequence
    else Awake State
        Engine->>Engine: Update UI Status (Green)
    end

Technical Implementation

The DrowsinessDetector Class

The system is encapsulated in a class to maintain the “state” of the user over time.

1. Temporal Analysis via deque

We use a collections.deque to store the history of eye states. A deque (double-ended queue) is used because it has a maxlen property. When the 16th frame arrives, the 1st frame is automatically deleted. This creates a Sliding Window of the last $0.5$ seconds of video (assuming 30 FPS).

2. The Decision Matrix

The engine doesn’t just check if eyes are closed; it evaluates the evidence using two different mathematical paths:

Path A: The Ratio Check (The “Drowsiness” Path) $\text{State}=\frac{\sum (\text{Closed Frames in Window})}{\text{Total Window Size}}$ If $\text{State}\ge 0.6$, the user is considered drowsy. This catches “slow-motion” fatigue where the user’s eyes are flickering.

Path B: The Streak Check (The “Nodding Off” Path) The system scans the end of the deque for a continuous sequence of True (closed) values. If $\text{Streak}\ge 8$, an immediate alert is triggered. This catches the moment a user’s head drops and eyes stay shut.

User Interface (UI) Overlay

The system uses cv2.putText to give the user real-time feedback:

• Green Text: “EYES OPEN - ALERT” $\to$ System is healthy and user is awake.
• Orange Text: “EYES CLOSING…” $\to$ Eyes are closed, but thresholds haven’t been hit yet.
• Red Text: “DROWSINESS DETECTED!” $\to$ Alert triggered.

Related Components

• alert-mechanism
• hyperparameters

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Last Updated: 2026-05-03