AI Home Defect Detector
Budget: $10 – $30 USD
I’m building an AI-driven solution that keeps single-family homes healthy by spotting structural issues, plumbing problems, and electrical faults while the house is in everyday use. The goal is a system that can take inputs such as periodic smartphone photos, CCTV streams, or smart-sensor readings and flag likely defects in real time so owners receive clear, actionable alerts before small flaws turn into costly repairs.
Here is what I need from you:
• A trained model (or ensemble) able to classify and localise those three defect categories with high precision on typical residential imagery or sensor data.
• A lightweight inference pipeline that can run on consumer-grade hardware or be exposed through a cloud API.
• A simple web or mobile interface where a homeowner uploads images or connects existing cameras/sensors and immediately sees detection results, confidence scores, and recommended next steps.
• Documentation that lets a non-technical property manager understand setup, data requirements, and model limitations, plus a short maintenance guide for future retraining.
Acceptance criteria
• Minimum 90 % precision and recall on a validation set covering structural, plumbing, and electrical examples common to single-family homes.
• Latency under two seconds per image on an average laptop CPU.
• Clear separation of false positives and genuine defects in the final report.
If you have experience with computer vision, edge AI, or smart-home integrations, I’d love to see a concise portfolio link or demo video when you bid.
Here is what I need from you:
• A trained model (or ensemble) able to classify and localise those three defect categories with high precision on typical residential imagery or sensor data.
• A lightweight inference pipeline that can run on consumer-grade hardware or be exposed through a cloud API.
• A simple web or mobile interface where a homeowner uploads images or connects existing cameras/sensors and immediately sees detection results, confidence scores, and recommended next steps.
• Documentation that lets a non-technical property manager understand setup, data requirements, and model limitations, plus a short maintenance guide for future retraining.
Acceptance criteria
• Minimum 90 % precision and recall on a validation set covering structural, plumbing, and electrical examples common to single-family homes.
• Latency under two seconds per image on an average laptop CPU.
• Clear separation of false positives and genuine defects in the final report.
If you have experience with computer vision, edge AI, or smart-home integrations, I’d love to see a concise portfolio link or demo video when you bid.