Problem
Industrial inspection teams need a faster way to identify defects without relying only on manual visual review.
Case Study
VisionInspect - Prompt-Conditioned Industrial Defect Segmentation System
Multimodal computer vision pipeline for automated drywall crack and seam segmentation using prompt-aware deep learning.
Business Impact
Creates a repeatable inspection workflow that can reduce manual review time and improve defect visibility.
Computer VisionSemantic SegmentationMultimodal AIMLOpsIndustrial AI
Solution
Built a prompt-conditioned computer vision pipeline for drywall crack and seam segmentation with tracked experiments.
Business Impact
Creates a repeatable inspection workflow that can reduce manual review time and improve defect visibility.
Delivery Notes
What this proves for a client project.
The goal is not to copy this exact product. It is to show the kind of product thinking, backend structure, AI workflow, and shipping discipline that can transfer to your business.
Built prompt-conditioned segmentation pipeline for drywall crack and seam detection using CLIPSeg and SAM
Designed reproducible ML workflow with DVC, MLflow, and DagsHub for experiment tracking and evaluation
Implemented pseudo-mask generation pipeline for weakly supervised segmentation from box-only datasets
Optimized CLIPSeg + CNN refiner architecture with Focal Tversky loss for thin-structure defect segmentation
Developed automated binary mask export system with benchmark evaluation using IoU and Dice metrics
Technology Stack
Tools used after the business case was clear.
PyTorchCLIPSegSAMOpenCVMLflowDVCDagsHubNumPy
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Share one workflow that takes too much manual effort. I will map where AI can help, what should stay human-reviewed, and what a first useful version could include.
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