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"Depth-Guard" – 3D Spatial Occupancy monitor Challenge -2

 

Project Title: "Depth-Guard" – 3D Spatial Occupancy Monitor

1. The Problem

In a smart warehouse, a robot needs to know if a loading zone is clear or occupied. A 2D camera alone can’t tell the difference between a "flat picture of a box" on the floor and an "actual 3D box."

The Goal: Build a Python-based system that uses Computer Vision and Depth Perception (AI 3D) to identify objects and determine their 3D volume (Size) and Distance from the camera.

2. Intern Tasks

  1. Object Detection: Use a pre-trained model (like YOLOv8) to draw 2D boxes around objects.

  2. Depth Mapping: Use a depth estimation model (like MiDaS or a simulated Stereo-depth feed) to calculate how far each object is.

  3. Occupancy Logic: If an object is closer than 1 meter and larger than a specific volume, mark the zone as "BLOCKED."

  4. Alert System: Print a warning if the 3D space is too crowded.

3. Sample Datasets (Simulation)

Since interns may not have 3D cameras (LiDAR/RGB-D), provide these two JSON-style datasets to simulate the camera feed.

Dataset A: Vision Feed (Object & Depth)

This dataset simulates what the AI "sees" in a single frame.

Object IDLabel2D Bounding Box (x,y,w,h)Avg. Depth Value (Meters)
Obj_101"Cardboard Box"[100, 200, 50, 50]0.8m
Obj_102"Human"[400, 150, 60, 180]2.5m
Obj_103"Pallet"[600, 500, 100, 30]1.2m
Obj_104"Small Tool"[250, 300, 10, 10]0.5m

Dataset B: Logic Constraints

The "Rules" the AI must follow to make decisions.

ParameterValueRule
CRITICAL_DISTANCE1.0mAny object closer than this is a "Hazard."
MIN_VOLUME_SIZE20cm³Ignore small objects (like dust or tiny tools).
ZONESZone A (0-2m)Categorize objects based on depth ranges.

4. Success Metrics for Interns

The interns must write a script that processes the data and outputs a Status Report:

Frame Analysis:

  • Obj_101 (Box): 0.8m away. STATUS: HAZARD (Inside 1.0m limit).

  • Obj_104 (Tool): 0.5m away. STATUS: IGNORE (Below minimum volume size).

  • Obj_102 (Human): 2.5m away. STATUS: SAFE (Zone B).

Final Decision: 🚩 Loading Zone Blocked

Simple Tech Recommendation

  • Language: Python.

  • Libraries: OpenCV (for image processing), NumPy (for depth math), and Matplotlib (to plot the objects in a 3D scatter plot).

  • AI Model (Optional): If they want to go beyond the sample data, suggest using MediaPipe or YOLOv8 for real-time webcam testing.

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