SensVX

Manual inspection in cable manufacturing is slow, error-prone, and costly — defects escape human reviewers and cause downtime.

The work started on the factory floor, not in a notebook: studying how defects actually appear on the line, which classes matter economically, and where cameras could physically mount. Dataset engineering followed — collecting images from real production runs, annotating defect classes manually, then cleaning and augmenting to survive real-world lighting variance.

• Maintaining detection reliability under changing line lighting and speed
• Building a defect taxonomy that matches how the factory actually classifies waste
• Keeping inference fast enough to be useful on a moving line
• Integrating AI output into an operator workflow people trust

A computer-vision platform: camera capture → frame processing → YOLO detection → defect classification → live dashboard, integrated with factory hardware.

• Deployed against real production-line conditions in cable manufacturing
• Continuous automated inspection replacing fatigue-limited manual checking
• Full traceability: every detection stored, classified, and reviewable
• Verified performance metrics to be published after production validation

• Dataset quality beats model sophistication — most gains came from better data
• Industrial AI is an integration problem as much as an ML problem
• Operator trust is a design requirement, not an afterthought