383 lines
19 KiB
Markdown
383 lines
19 KiB
Markdown
# Models Architecture & Prediction Flow - Comprehensive Diagram
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## 📊 Models Overview
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### Current Models in Use
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| Model | Type | Size | Format | Purpose | Location |
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|-------|------|------|--------|---------|----------|
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| **YOLOv8n** | Deep Learning | 6.3 MB | PyTorch (.pt) | Base model (downloaded if needed) | `models/yolov8n.pt` |
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| **YOLOv8n ONNX** | Deep Learning | 13 MB | ONNX Runtime | Object Detection (Person, Phone) | `models/yolov8n.onnx` |
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| **Haar Cascade Face** | Traditional ML | ~908 KB | XML (Built-in) | Face Detection | OpenCV built-in |
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| **Haar Cascade Eye** | Traditional ML | ~900 KB | XML (Built-in) | Eye Detection (PERCLOS) | OpenCV built-in |
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**Total Model Size**: ~15.2 MB (excluding built-in OpenCV cascades)
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---
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## 🔄 Complete Prediction Flow Diagram
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ VIDEO INPUT (640x480 @ 30 FPS) │
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│ Camera or Video File │
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└───────────────────────────────┬─────────────────────────────────────────────┘
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│
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▼
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┌───────────────────────┐
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│ Frame Capture Loop │
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│ (Every Frame) │
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└───────────┬───────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ FRAME PROCESSING DECISION │
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│ if (frame_idx % 2 == 0): Process │
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│ else: Use Last Predictions (Smooth Video) │
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└───────────────────┬───────────────────────────────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ PARALLEL PROCESSING │
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│ │
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│ ┌────────────────────┐ ┌──────────────────────┐ │
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│ │ FACE ANALYSIS │ │ OBJECT DETECTION │ │
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│ │ (OpenCV) │ │ (YOLOv8n ONNX) │ │
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│ └─────────┬──────────┘ └──────────┬───────────┘ │
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│ │ │ │
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│ ▼ ▼ │
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│ ┌────────────────────┐ ┌──────────────────────┐ │
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│ │ Haar Cascade Face │ │ Input: 640x640 RGB │ │
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│ │ Size: ~908 KB │ │ Output: 8400 boxes │ │
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│ │ │ │ Classes: 80 COCO │ │
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│ │ • Face Detection │ │ Filter: [0, 67] │ │
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│ │ • Head Pose Calc │ │ • Person (0) │ │
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│ └─────────┬──────────┘ │ • Cell Phone (67) │ │
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│ │ └──────────┬───────────┘ │
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│ ▼ │ │
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│ ┌────────────────────┐ │ │
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│ │ Haar Cascade Eye │ │ │
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│ │ Size: ~900 KB │ │ │
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│ │ │ │ │
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│ │ • Eye Detection │ │ │
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│ │ • PERCLOS Calc │ │ │
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│ └─────────┬──────────┘ │ │
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│ │ │ │
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│ ▼ ▼ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ FACE ANALYSIS RESULTS │ │
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│ │ • present: bool │ │
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│ │ • perclos: float (0.0-1.0) │ │
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│ │ • head_yaw: float (degrees) │ │
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│ │ • head_pitch: float (degrees) │ │
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│ └──────────────────────────────────────────────┘ │
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│ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ OBJECT DETECTION RESULTS │ │
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│ │ • bboxes: array[N, 4] │ │
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│ │ • confs: array[N] │ │
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│ │ • classes: array[N] (0=person, 67=phone) │ │
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│ └──────────────────────────────────────────────┘ │
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└───────────────────┬──────────────────────────────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ SEATBELT DETECTION (Every 6th Frame) │
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│ │
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│ Input: Object Detection Results │
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│ Method: YOLO Person + Position Analysis │
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│ │
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│ • Find person in detections │
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│ • Calculate aspect ratio (height/width) │
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│ • Check position (driver side) │
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│ • Heuristic: upright + reasonable size = seatbelt │
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│ │
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│ Output: has_seatbelt (bool), confidence (float) │
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└───────────────────┬──────────────────────────────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ ALERT DETERMINATION │
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│ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ 1. DROWSINESS │ │
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│ │ Condition: perclos > 0.3 │ │
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│ │ Threshold: 30% eye closure │ │
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│ └──────────────────────────────────────────────┘ │
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│ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ 2. DISTRACTION │ │
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│ │ Condition: |head_yaw| > 20° │ │
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│ │ Threshold: 20 degrees │ │
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│ └──────────────────────────────────────────────┘ │
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│ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ 3. DRIVER ABSENT │ │
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│ │ Condition: face_data['present'] == False │ │
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│ │ Immediate detection │ │
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│ └──────────────────────────────────────────────┘ │
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│ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ 4. PHONE DETECTED │ │
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│ │ Condition: class == 67 in detections │ │
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│ │ Confidence: > 0.5 │ │
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│ └──────────────────────────────────────────────┘ │
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│ │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ 5. NO SEATBELT │ │
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│ │ Condition: !has_seatbelt && conf > 0.3 │ │
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│ │ Heuristic-based │ │
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│ └──────────────────────────────────────────────┘ │
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└───────────────────┬──────────────────────────────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ TEMPORAL SMOOTHING (Alert Persistence) │
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│ │
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│ For each alert: │
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│ • If triggered: Set ACTIVE, reset counter │
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│ • If not triggered: Increment counter │
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│ • Clear after N frames: │
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│ - Drowsiness: 10 frames (~0.3s) │
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│ - Distraction: 8 frames (~0.27s) │
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│ - Driver Absent: 5 frames (~0.17s) │
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│ - Phone: 5 frames (~0.17s) │
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│ - Seatbelt: 8 frames (~0.27s) │
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└───────────────────┬──────────────────────────────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ FRAME ANNOTATION │
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│ │
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│ • Draw bounding boxes (Person: Green, Phone: Magenta)│
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│ • Draw face status (PERCLOS, Yaw) │
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│ • Draw active alerts (Red text) │
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│ • Overlay on original frame │
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└───────────────────┬──────────────────────────────────┘
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│
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▼
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┌───────────────────────────────────────────────────────┐
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│ OUTPUT TO STREAMLIT UI │
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│ │
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│ • Annotated frame (RGB) │
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│ • Alert states (ACTIVE/Normal) │
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│ • Statistics (FPS, Frames Processed) │
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│ • Recent logs │
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└───────────────────────────────────────────────────────┘
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```
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---
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## 📐 Detailed Model Specifications
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### 1. YOLOv8n (Nano) - Object Detection
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**Architecture**:
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- Backbone: CSPDarknet
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- Neck: PANet
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- Head: YOLO Head
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**Input**:
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- Size: 640x640 RGB
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- Format: Float32, normalized [0, 1]
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- Shape: (1, 3, 640, 640)
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**Output**:
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- Shape: (1, 84, 8400)
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- 84 = 4 (bbox) + 80 (COCO classes)
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- 8400 = anchor points
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- Format: Float32
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**Classes Detected**:
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- Class 0: Person
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- Class 67: Cell Phone
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**Performance** (Raspberry Pi 5):
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- Inference Time: ~50-80ms per frame
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- Memory: ~200-300 MB
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- FPS: 12-20 (with frame skipping)
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**Optimization**:
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- ONNX Runtime (CPU optimized)
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- Frame skipping (every 2nd frame)
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- Class filtering (only person & phone)
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---
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### 2. OpenCV Haar Cascade - Face Detection
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**Type**: Traditional Machine Learning (Viola-Jones)
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**Face Cascade**:
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- Size: ~908 KB
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- Features: Haar-like features
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- Stages: 22 stages
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- Input: Grayscale image
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- Output: Face bounding boxes (x, y, width, height)
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**Eye Cascade**:
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- Size: ~900 KB
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- Features: Haar-like features
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- Input: Face ROI (grayscale)
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- Output: Eye bounding boxes
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**Performance**:
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- Inference Time: ~10-20ms per frame
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- Memory: ~50 MB
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- Accuracy: ~85-90% for frontal faces
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**Limitations**:
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- Best for frontal faces
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- Struggles with side profiles
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- Sensitive to lighting
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---
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## 🔢 Processing Statistics
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### Frame Processing Rate
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- **Camera FPS**: 30 FPS (target)
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- **Processing Rate**: Every 2nd frame (15 FPS effective)
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- **Face Analysis**: Every processed frame
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- **Object Detection**: Every processed frame
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- **Seatbelt Detection**: Every 6th frame (5 FPS)
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### Memory Usage
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- **YOLO ONNX Model**: ~13 MB (loaded)
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- **OpenCV Cascades**: Built-in (~2 MB)
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- **Runtime Memory**: ~300-500 MB
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- **Total**: ~800 MB (Raspberry Pi 5)
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### CPU Usage
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- **Face Analysis**: ~15-20%
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- **Object Detection**: ~30-40%
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- **Frame Processing**: ~10-15%
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- **Total**: ~55-75% (Raspberry Pi 5)
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---
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## 🎯 Prediction Accuracy
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| Feature | Method | Accuracy | Notes |
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|---------|--------|----------|-------|
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| **Face Detection** | Haar Cascade | 85-90% | Frontal faces only |
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| **Eye Detection** | Haar Cascade | 80-85% | PERCLOS calculation |
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| **Head Pose** | Position-based | 75-80% | Simplified heuristic |
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| **Person Detection** | YOLOv8n | 90-95% | High accuracy |
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| **Phone Detection** | YOLOv8n | 85-90% | Good for visible phones |
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| **Seatbelt Detection** | Heuristic | 70-75% | Position-based estimate |
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---
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## 🔄 Data Flow Summary
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```
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Frame (640x480)
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│
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├─→ Face Analysis (OpenCV)
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│ ├─→ Face Detection (Haar Cascade)
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│ ├─→ Eye Detection (Haar Cascade)
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│ └─→ Head Pose Calculation
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│
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├─→ Object Detection (YOLOv8n ONNX)
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│ ├─→ Resize to 640x640
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│ ├─→ ONNX Inference
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│ ├─→ Parse Output (8400 detections)
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│ └─→ Filter (Person, Phone)
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│
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└─→ Seatbelt Detection (Heuristic)
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├─→ Find Person in Detections
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├─→ Analyze Position
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└─→ Calculate Confidence
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↓
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Alert Logic
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├─→ Drowsiness (PERCLOS > 0.3)
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├─→ Distraction (|Yaw| > 20°)
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├─→ Driver Absent (!present)
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├─→ Phone Detected (class == 67)
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└─→ No Seatbelt (!has_seatbelt)
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↓
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Temporal Smoothing
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└─→ Persistence Counters
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└─→ Clear after N frames
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↓
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Annotated Frame
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└─→ Display in Streamlit UI
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```
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---
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## 📊 Model Size Breakdown
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```
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Total Storage: ~15.2 MB
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├── YOLOv8n.pt: 6.3 MB (PyTorch - source)
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├── YOLOv8n.onnx: 13 MB (ONNX Runtime - used)
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└── OpenCV Cascades: Built-in (~2 MB)
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├── Face Cascade: ~908 KB
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└── Eye Cascade: ~900 KB
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```
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**Note**: Only ONNX model is loaded at runtime. PyTorch model is only used for conversion.
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---
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## ⚡ Performance Optimization Strategies
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1. **Frame Skipping**: Process every 2nd frame (50% reduction)
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2. **ONNX Runtime**: Faster than PyTorch on CPU
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3. **Class Filtering**: Only detect relevant classes (person, phone)
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4. **Seatbelt Throttling**: Process every 6th frame
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5. **Smooth Video**: Show all frames, overlay predictions
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6. **Memory Management**: Limit log entries, efficient arrays
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---
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## 🎨 Visual Representation
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### Model Loading Sequence
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```
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Application Start
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│
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├─→ Load YOLOv8n ONNX (13 MB)
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│ └─→ ONNX Runtime Session
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│
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└─→ Load OpenCV Cascades
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├─→ Face Cascade (~908 KB)
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└─→ Eye Cascade (~900 KB)
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Total Load Time: ~2-3 seconds
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```
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### Per-Frame Processing Time
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```
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Frame Capture: ~1-2 ms
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│
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├─→ Face Analysis: ~15-20 ms
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│ ├─→ Face Detection: ~10 ms
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│ └─→ Eye Detection: ~5 ms
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│
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├─→ Object Detection: ~50-80 ms
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│ ├─→ Preprocessing: ~5 ms
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│ ├─→ ONNX Inference: ~40-70 ms
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│ └─→ Post-processing: ~5 ms
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│
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└─→ Seatbelt Detection: ~2-3 ms (every 6th frame)
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Total: ~65-100 ms per processed frame
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Effective FPS: 10-15 FPS (with frame skipping)
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```
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---
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This comprehensive diagram shows the complete architecture, model sizes, prediction flow, and performance characteristics of the Driver DSMS ADAS system optimized for Raspberry Pi 5.
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