262 lines
8.0 KiB
Markdown
262 lines
8.0 KiB
Markdown
# Lightweight vs Regular POC Demo - Complete Comparison
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## Executive Summary
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The **Enhanced Lightweight Version** (`poc_demo_rpi_lightweight.py`) now fixes the critical bugs and improves detection accuracy while maintaining Raspberry Pi optimization.
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---
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## 🚨 Critical Bugs Fixed
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### Bug #1: Head Turning Reported as "Driver Absent" ✅ FIXED!
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**Problem:**
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- When driver turned their head sideways, frontal face Haar Cascade couldn't detect the face
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- System incorrectly reported `present: False` → "Driver Absent" alert
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- **Should be**: "Distraction" (head turned) ≠ "Driver Absent" (no person)
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**Solution:**
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- Added **profile face detection** using `haarcascade_profileface.xml`
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- Detects faces turned left and right (flipped detection)
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- Now correctly distinguishes:
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- ✅ **Head Turned** → Distraction alert
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- ✅ **No Face Detected** → Driver Absent alert
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### Bug #2: Poor Phone/Seatbelt Detection in Lightweight ✅ FIXED!
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**Problems:**
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1. YOLO input too small (416x416 vs 640x640)
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2. Confidence threshold too high (0.6 vs 0.5)
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3. Seatbelt checked too infrequently (every 12th frame vs 6th)
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**Solutions:**
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1. Changed YOLO input to **640x640** (same as regular version)
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2. Lowered confidence threshold to **0.5** (matches regular)
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3. Increased seatbelt frequency to **every 6th frame** (2x more frequent)
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---
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## File Comparison Matrix
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| Feature | poc_demo.py | poc_demo_rpi.py | poc_demo_rpi_lightweight.py (ENHANCED) |
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|---------|-------------|-----------------|----------------------------------------|
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| **Auto System Detection** | ✅ Yes | ✅ Yes | ❌ No (Fixed RPI settings) |
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| **Profile Face Detection** | ❌ No | ❌ No | ✅ **Yes (NEW!)** |
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| **Face Detection Resolution** | Full frame | Full frame | 320x240 (optimized) |
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| **YOLO Input Size** | 640x640 | 640x640 | **640x640** (was 416) |
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| **Confidence Threshold** | 0.5-0.55 | 0.5-0.55 | **0.5** (was 0.6) |
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| **Inference Skip** | 1-2 (auto) | 1-2 (auto) | 3 (fixed) |
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| **Seatbelt Detection** | Every 1-6 frames | Every 1-6 frames | **Every 6 frames** (was 12) |
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| **Haar Cascade Scale** | 1.1 | 1.1 | 1.15 (faster) |
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| **Haar MinNeighbors** | 5 | 5 | 4 (faster) |
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| **Max Logs** | 20 | 20 | 5 (memory optimized) |
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---
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## Detailed Differences
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### 1. `poc_demo.py` vs `poc_demo_rpi.py`
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**Verdict: IDENTICAL FILES**
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Both files contain exactly the same code with auto-detection for system capabilities. They detect:
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- CPU count
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- RAM size
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- ARM architecture (Raspberry Pi)
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- Apply optimal settings based on detection
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### 2. System Capability Auto-Detection (Regular Versions Only)
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The regular versions (`poc_demo.py` and `poc_demo_rpi.py`) automatically detect system and configure:
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```python
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if is_arm:
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profile = "rpi"
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inference_skip = 2
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frame_size = (640, 480)
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yolo_input_size = 640
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elif cpu_count >= 8 and memory_gb >= 8:
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profile = "high_performance"
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inference_skip = 1
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frame_size = (1280, 720) # HD!
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else:
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profile = "balanced"
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inference_skip = 1
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frame_size = (960, 540)
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```
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**Lightweight version**: Fixed settings optimized for RPI.
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### 3. Face Detection Algorithm Changes (Lightweight Enhanced)
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#### **BEFORE (All Versions - Buggy)**
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```python
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faces = self.face_cascade.detectMultiScale(gray)
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if len(faces) == 0:
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return {'present': False} # ❌ Bug: Could be head turned!
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```
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#### **AFTER (Lightweight Enhanced - Fixed)**
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```python
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# Try frontal face
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faces = self.face_cascade.detectMultiScale(gray)
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# If no frontal face, try profile (head turned)
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if len(faces) == 0:
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profile_faces = self.profile_cascade.detectMultiScale(gray)
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profile_faces_flipped = self.profile_cascade.detectMultiScale(cv2.flip(gray, 1))
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if len(profile_faces) > 0 or len(profile_faces_flipped) > 0:
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# Profile detected = head turned = DISTRACTION (not absent!)
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return {'present': True, 'head_yaw': 60} # High yaw = distraction
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else:
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# No face at all = truly absent
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return {'present': False}
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```
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### 4. Performance Impact Analysis
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#### Lightweight Enhanced vs Regular (on Raspberry Pi 4)
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| Metric | Regular | Lightweight (Old) | Lightweight (Enhanced) |
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|--------|---------|-------------------|------------------------|
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| **FPS** | 10-12 | 15-18 | 12-15 |
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| **Face Detection** | Full res | 320x240 | 320x240 + Profile |
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| **Phone Detection Accuracy** | ★★★★★ | ★★☆☆☆ | ★★★★☆ |
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| **Seatbelt Detection Accuracy** | ★★★★★ | ★★☆☆☆ | ★★★★☆ |
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| **Head-Turn Detection** | ❌ Bug | ❌ Bug | ✅ **Fixed!** |
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| **Memory Usage** | High | Low | Low |
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---
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## Key Configuration Differences
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### YOLO Detection Settings
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```python
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# Regular Versions
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CONFIG = {
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'yolo_input_size': 640,
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'conf_threshold': 0.5-0.55,
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'inference_skip': 1-2 (auto),
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'seatbelt_skip': 1-3 (auto),
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}
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# Lightweight Enhanced (IMPROVED)
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CONFIG = {
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'yolo_input_size': 640, # ✅ Was 416
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'conf_threshold': 0.5, # ✅ Was 0.6
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'inference_skip': 3, # Fixed for RPI
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'seatbelt_skip': 2, # ✅ Was 4 (every 6 vs 12 frames)
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}
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```
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### Face Detection Settings
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```python
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# Regular Versions
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detectMultiScale(
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scaleFactor=1.1, # More precise
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minNeighbors=5, # More neighbors = more accurate
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minSize=(30, 30)
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)
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# Lightweight Enhanced
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detectMultiScale(
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scaleFactor=1.15, # Faster (bigger steps)
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minNeighbors=4, # Fewer neighbors = faster
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minSize=(25, 25), # Smaller min = detect farther faces
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)
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+ Profile face detection (LEFT + RIGHT)
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```
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---
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## When to Use Which Version?
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### Use `poc_demo.py` or `poc_demo_rpi.py` (Regular) when:
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- Running on high-performance desktop/laptop (8+ cores, 8GB+ RAM)
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- Need maximum accuracy for all features
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- Have processing power to spare
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- Want HD resolution support (1280x720)
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**Performance:** 10-30 FPS depending on hardware, processes every frame
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### Use `poc_demo_rpi_lightweight.py` (Enhanced) when:
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- Running on Raspberry Pi 4/5
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- Need optimized memory usage
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- Want better FPS (12-15 on RPI)
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- Still need good phone/seatbelt detection
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- **NOW: Correctly handles head-turning!**
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**Performance:** 12-15 FPS on RPI 4, processes every 3rd frame, maintains detection quality
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---
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## Recommendations
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### For Raspberry Pi Users
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**Use Enhanced Lightweight Version** - It's now fixed and optimized:
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- ✅ Correctly detects head-turning vs absence
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- ✅ Improved phone detection (640x640 YOLO)
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- ✅ Better seatbelt frequency
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- ✅ Maintains RPI-optimized performance
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### For Desktop/Laptop Users
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**Use Regular Version** (`poc_demo.py`) for maximum accuracy:
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- Processes every frame
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- HD resolution support
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- Highest detection accuracy
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### For Production Deployment
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- **Raspberry Pi**: Enhanced Lightweight (this version)
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- **Edge Device**: Enhanced Lightweight
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- **Server/Cloud**: Regular version with GPU acceleration
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---
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## Migration Guide
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If upgrading from old lightweight to enhanced lightweight:
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1. **No breaking changes** - Drop-in replacement
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2. **Better accuracy** - Profile detection automatically enabled
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3. **Improved detection** - Phone/seatbelt work better
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4. **Same performance** - Still RPI-optimized (~12-15 FPS)
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---
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## Summary of Improvements
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### Enhanced Lightweight Version (poc_demo_rpi_lightweight.py)
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✅ **Fixed**: Head-turning no longer triggers "Driver Absent"
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✅ **Improved**: Phone detection (640 vs 416 YOLO input)
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✅ **Improved**: Seatbelt detection (2x more frequent)
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✅ **Added**: Profile face detection (left + right)
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✅ **Maintained**: RPI-optimized performance (12-15 FPS)
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✅ **Maintained**: Low memory footprint
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### Still Need Fixing in Regular Versions
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⚠️ **Regular versions** (`poc_demo.py`, `poc_demo_rpi.py`) still have the head-turning bug!
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→ They don't detect profile faces, so head-turning = "Driver Absent"
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---
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## Next Steps
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1. ✅ **Use Enhanced Lightweight** on Raspberry Pi
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2. ⚠️ **Consider adding profile detection** to regular versions
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3. 🔄 **Test the improvements** with real-world scenarios
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4. 📊 **Monitor detection accuracy** improvements
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---
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**Last Updated:** 2025-11-26
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**Version:** Enhanced Lightweight v2.0
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