Inspiration
A suspicious mark on a circuit board can be a real defect, glare or alignment error. Inspection operators need a useful next step when the first image is inconclusive, along with a record of why the system reached its decision.
We are Team Update — Shikhar Goel and Ishpreet Singh. We built CircuitSleuth to investigate visual uncertainty with OpenCV 5 and an auditable tool loop.
What it does
An operator supplies a reference board and a test image, or selects a seeded synthetic demonstration. OpenCV 5 aligns the images and detects candidate changes. A bounded planning policy chooses the next action for each open region using uncertainty, severity, evidence quality, estimated information gain and cost.
Alignment concerns can trigger local registration. Illumination concerns can trigger a threshold-based confound test. A separately supplied capture enables an independent-view comparison. A missing capture can trigger a request for more evidence and human review. Unreliable alignment can end with “not evaluable.”
The interface streams actual plan and tool events, shows image evidence and allows operator decisions. Reports include verdicts, limitations and the review trail. Reruns create a new inspection while preserving the original run.
How we built it
The application uses Python 3.13, FastAPI, OpenCV 5.0.0, NumPy, SQLite, React, TypeScript and Vite. OpenCV performs feature matching, RANSAC registration, photometric verification, ECC refinement, difference analysis, topology and regional verification. The same eight inspection tools are exposed over MCP.
Our deterministic planner is transparent and budget-bounded. Measured visual flags and accumulated evidence change subsequent tool calls; a result with decisive evidence can move directly to verification. This is an active perception workflow with human control, without an LLM dependency.
The Docker container builds and runs in Linux CI. We prepared an EC2 deployment with encrypted EBS, CloudWatch logs and an authenticated Systems Manager tunnel. Actual AWS execution remains pending: the account currently redirects EC2 to its account-setup page. We do not claim local or CI execution as AWS proof. No COOL or AWS Graviton implementation is claimed.
Challenges we addressed
Image registration can appear convincing while aligning periodic traces to the wrong features. We verify hypotheses photometrically and apply quality gates. We also corrected ECC transform direction and tightened local alignment evidence so unconstrained fitting cannot simply erase a real defect. Conflicting local and separate-view evidence is sent to review.
Reprocessing existing pixels does not create optical detail. Our interface and reports distinguish those tools from a separately supplied capture.
Results and verification
On 30 seeded synthetic inspections, using the same current OpenCV core in fixed and adaptive modes:
| Metric | Fixed inspection | Adaptive verification |
|---|---|---|
| Macro reported precision | 0.5208 | 0.8627 |
| Macro reported recall | 0.8933 | 0.7822 |
| Macro reported F1 | 0.5751 | 0.7155 |
| Reported false positives | 50 | 10 |
| Candidate verdicts requiring review | 0% | 27.27% |
The 80% reduction in reported false positives comes with lower reported recall and added human-review work. Matching uses IoU 0.15. These are synthetic benchmark results, not production PCB accuracy. The final audit reproduced all 30 non-timing outputs exactly.
Backend regression tests, frontend lint/build, actual MCP stdio calls and container HTTP checks cover the working application. Six demo cases exercise clear defects, a clean board, glare, alignment failure, missing views and a limited tool budget. Reports, raw events, planning references and image crops are exportable with hashes.
What we learned and what is next
An inspection system must preserve uncertainty as carefully as it records a positive finding. Our research also exposed substantial domain gap on a small external DeepPCB check and existing wrong-board acceptance in some mismatch controls. The current system is a supervised demonstration; real manufacturing validation, stronger board identity, tiny-defect sensitivity and an integrated capture rig remain future work.
Before final submission we still need the actual AWS run, judge-accessible source/demo arrangements and the required team demonstration video. Video production is deferred by our team for now. The pitch, technical report, architecture, dependency locks, testing instructions and source package are prepared.