COMPUTER VISION · SC-30
Computer Vision Waste Detection & Sorting System
A computer-vision system for detecting, counting and classifying waste items and routing uncertain detections to review.
Starting point
Detection, classification and routing need an explicit path for ambiguous objects and changing conditions.
01 · BUSINESS PROBLEM
Visual automation fails when confidence is treated as certainty.
Detection, classification and routing need an explicit path for ambiguous objects and changing conditions.
Ambiguous classes
Visually similar materials create costly misclassification.
Changing scenes
Lighting, occlusion and camera position alter model confidence.
No review path
Low-confidence detections need a controlled fallback.
02 · DECISION LOGIC
Detect, classify, route and review.
The model output is treated as one stage in an operating process, not as an unquestioned answer.
Decision sequence
Each stage answers a different operating question
Standardize frames and camera input.
Locate and classify visible objects.
Map confident classes to an action.
Escalate ambiguous detections.
Decision rule — Confidence should control the action path, not disappear inside the model.
03 · WHAT CHANGED
A vision model connected to an operational review loop.
Detection, confidence thresholds, routing and traceability remain explicit.
Standardize the image input.
Detect and classify multiple objects per frame.
Route high-confidence classes automatically.
Retain low-confidence cases for review and learning.
04 · ARCHITECTURE
A modular path from input to decision.
Inputs → preparation → core logic → validation → decision output
SC-30 · SYSTEM ARCHITECTURE
Inputs → preparation → core logic → validation → decision output
Public portfolio implementation
Inputs
Source signals
Capture the operating inputs required by the system. [Python]
Preparation layer
Normalize context and create a stable analytical contract. [PyTorch]
Core system
Core engine
Run the main analytical or automation logic. [Ultralytics YOLO]
Decision logic
Apply the rule, model or orchestration logic that changes the decision. [OpenCV]
Validation
Validation
Test outputs against explicit quality criteria. [FastAPI]
Controls
Keep approvals, thresholds or constraints visible. [NumPy]
Decision output
Decision output
Expose the result in a form the user can act on. [Pandas]
Monitoring
Record outcomes, exceptions and evidence for iteration. [Plotly]
Integration boundaries
Python
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
PyTorch
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
Ultralytics YOLO
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
05 · EVIDENCE & ECONOMICS
Measure what changes the decision.
Public implementation, inspectable technical proof and decision-focused validation.
Detection quality
Classes
5
Representative public example.
mAP50
0.86
Representative public example.
Validation frames
1k
Representative public example.
Review coverage
Confidence bands
3
Representative public example.
Output routes
4
Representative public example.
Reference economics
8 h/d
Reference scenario
20%
Illustrative improvement
1.6 h/d
Decision value
06 · TECHNICAL PROOF
Review the code behind the project.
Tools used
BUSINESS CONCLUSION
Computer vision creates value when model confidence changes the operating action.
The useful system combines detection quality with routing, review and continuous evidence collection.