Forum#4: Adapting the Sentinel Vision Model for Industrial Defect Detection (ILS)

Hello fellow challengers! Welcome to my 4th update.

In my original application, I proposed a specific goal for my Industrial Defect Detection (ILS) system: "I will deploy a quantized MobileNet-SSD model within a headless Docker container to detect safety gear (glasses/vests) and 'Red Zone' incursions. To manage the 2GB RAM limit and avoid swap exhaustion, the vision pipeline will use a motion-triggered inference engine."

Today, I’m excited to share the step-by-step progress of this experiment using the Arduino UNO Q, focusing on how to securely deploy a custom vision "Brick" while strictly managing hardware resources.

1. Hardware Setup: Using a USB Camera on the UNO Q

The new Arduino UNO Q features a fantastic dual-brain architecture. It pairs an STM32 microcontroller (for real-time hardware control) with a Qualcomm Dragonwing quad-core processor running a full Debian Linux OS.

Because we have a full Linux environment, adding "eyes" to the system is incredibly straightforward:

• The Connection: The UNO Q features a high-speed USB-C port. By using a standard USB-C hub or a USB-A to USB-C adapter, I plugged in a standard UVC (USB Video Class) webcam.

• The OS Layer: Linux natively recognizes the camera. It automatically mounts it to /dev/video0. There is no need for complex microcontroller camera drivers; the MPU treats it exactly as a desktop computer would!

2. The Strategy: Motion-Triggered Inference

The Challenge: The UNO Q has 2GB of RAM. A continuous video feed running through a deep learning object detection model (like MobileNet-SSD) can easily consume 1.5GB+. When Linux runs out of RAM, it uses "swap" space on the eMMC storage. Constant swapping will quickly wear out the flash memory and crash the system (Swap Exhaustion).

The Novice-Friendly Solution: Imagine a security guard staring at an empty hallway. Instead of staring intently 100% of the time, the guard only focuses when they see movement. We will do the same:

1. The camera captures frames.

2. A lightweight "Motion Detection" algorithm compares the current frame to the previous one. This uses almost zero RAM.

3. If movement is detected, it "wakes up" the heavy AI model to check for Safety Vests, Glasses, or Red Zone incursions.

Data Flow Diagram

flowchart TD
    A[USB Camera /dev/video0] --> B(Capture Frame)
    B --> C{Is there Motion?}
    C -- No --> B
    C -- Yes --> D[Trigger AI Inference]
    D --> E[Quantized MobileNet-SSD]
    E --> F{Objects Detected?}
    F -- Safety Gear Missing --> G[Log Safety Violation]
    F -- Red Zone Breach --> H[Trigger Alarm on STM32 MCU]
    F -- All Clear --> B

3. Creating the Custom "Brick" Model

In the Arduino App Lab ecosystem, a "Brick" is a modular block of software. We are building a Custom AI Vision Brick.

To make this fit in our 2GB limit, we use a technique called Quantization. Normally, AI models use 32-bit floating-point numbers (high precision, large file size). By quantizing the MobileNet-SSD model to INT8 (8-bit integers), we shrink the model footprint from ~20MB down to ~4MB, drastically reducing the RAM required to load the model into memory while maintaining enough accuracy to detect high-contrast items like neon safety vests.

4. Step-by-Step Implementation Guide

Here is how I designed, coded, and implemented this on the UNO Q.

Step 1: Prepare the Headless Docker Environment

"Headless" simply means we are running the software in the background without a graphical desktop (no windows, no mouse pointer). This alone saves about 400MB of RAM!

We write a Dockerfile that installs only the bare minimum: Python, OpenCV (headless version), and our AI runtime (like TensorFlow Lite).

Step 2: The Motion-Triggered Code Logic

Here is a simplified look at the Python script running inside our Docker container. Notice how we only invoke the heavy detect_objects function if the motion threshold is met.

import cv2
import time

# Initialize USB Camera
cap = cv2.VideoCapture(0)
ret, frame1 = cap.read()
ret, frame2 = cap.read()

while cap.isOpened():
    # 1. Calculate absolute difference between frames for motion
    diff = cv2.absdiff(frame1, frame2)
    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)
    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
    
    # 2. Find contours of the motion
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    
    motion_detected = False
    for contour in contours:
        if cv2.contourArea(contour) > 5000: # Adjust sensitivity here
            motion_detected = True
            break
            
    # 3. Trigger the Heavy AI Model ONLY if motion is found
    if motion_detected:
        print("Motion detected! Triggering MobileNet-SSD...")
        # --> Run Quantized INT8 Inference Here <--
        # results = run_mobilenet_ssd(frame1)
        # check_red_zone_incursion(results)
    
    # Update frames
    frame1 = frame2
    ret, frame2 = cap.read()
    
    # Small sleep to free up CPU cycles
    time.sleep(0.1) 

Step 3: Deployment with Strict RAM Limits

To absolutely ensure we never hit swap exhaustion, we deploy our container using Docker Compose with strict memory limits enforced.

# docker-compose.yml
version: '3.8'
services:
  sentinel_vision:
    build: .
    devices:
      - "/dev/video0:/dev/video0" # Passthrough the USB camera
    deploy:
      resources:
        limits:
          memory: 800M # Strictly limit the container to 800MB RAM
    restart: unless-stopped

Step 4: Connecting MPU to MCU (The Output)

When the Linux side (MPU) detects a missing safety vest or a Red Zone incursion, it sends a simple serial payload to the real-time STM32 microcontroller (MCU) side of the UNO Q. The MCU then instantly flips a relay to sound an alarm or halt a machine, with zero latency!

Conclusion

By combining a headless Docker architecture, an INT8 quantized MobileNet-SSD model, and a motion-triggered inference gate, we completely bypass the standard bottlenecks of edge AI. We keep RAM usage under 1GB, protect the onboard eMMC from swap wear, and maintain rapid response times for industrial safety.

I would love to hear your feedback! 1. Has anyone else experimented with INT8 quantization on the UNO Q?

2. Are there better algorithms than cv2.absdiff for lightweight motion triggering in varying industrial lighting?

Let me know your thoughts below!