Underwater Crack Detection
Applies computer vision to inspection tasks where manual review is costly, risky, or inconsistent.
system visual
Protected or source media unavailable. Architecture preview used instead.
System Overview
Underwater concrete crack detection system using CNN/YOLO-style inspection workflows for structural safety.
Capstone/research system.
Implementation Signals
System flow
A compact view of how inputs move through processing, orchestration, validation, and output.
Inspection Images
Image Prep
Detection Windows
CNN / YOLO
Crack Review
Engineering decisions
Decision Record
Frame inspection as safety assistance
Problem: Manual underwater inspection is limited by risk and visibility.
Approach: Use CNN/YOLO-style detection to identify possible crack regions for review.
Tradeoff: Field deployment requires false-positive controls and environment robustness.
Outcome: A strong bridge between robotics roots and applied perception systems.
Results / Learnings
README
States high accuracy and precision, but no exact numeric value is provided.
Learning
Inspection AI needs robust review and false-positive handling.
Progressive depth
This page keeps the outcome and architecture visible first. Implementation stack, decisions, constraints, and media are available below so technical depth is opt-in rather than forced.
Adjacent systems
ROV2019 Navigation Stack
Underwater ROV navigation and hardware repository with PC-to-Arduino communication, camera configs, and engineering docs.
Repo: Includes navigation, arms, Arduino libraries, MQTT demo, camera configs, and engineering docs.
OpenLiDAR Safety Monitoring
3D computer vision safety monitoring pattern for operational environments using LiDAR-style perception and false-positive controls.
Detection: >98% detection accuracy, resume-provided.
Open