Automated Visual Inspection System for Fiber Optic Installation Validation Using AI and Computer Vision

Manual verification of fiber optic installation images is resource-intensive, time-consuming, and prone to inconsistencies, leading to delays and potential quality issues during infrastructure deployment. The client requires an efficient, automated process to ensure installation correctness across multiple locations and stages, reducing resource expenditure and increasing reliability.

A large utility company responsible for deploying fiber optic infrastructure for internet and telecommunication services, seeking to optimize installation quality assurance processes.

• Develop an AI-powered visual inspection system capable of automatically verifying installation images at multiple stages.
• Achieve a high accuracy rate (target > 90%) in detecting correct and incorrect installation states, surpassing manual verification benchmarks.
• Provide explainability for AI decisions through visual highlights to build user trust and facilitate validation.
• Enable rapid analysis and feedback on installation images through an accessible web interface for field contractors.
• Ensure secure handling and processing of sensitive installation data within cloud or hybrid environments.

• Image Analysis Module that processes photos, identifying key components and installation states.
• Deep Learning Model trained on labeled images to classify correct versus incorrect installations at defined checkpoints.
• Explainable AI component that highlights relevant image regions influencing the decision.
• Secure data transfer protocols and handling for large datasets, enabling efficient model training.
• Web application interface allowing users to upload images, view analysis results, and access explanation visualizations.
• Robust crossvalidation methodology to ensure high model accuracy and generalizability.

• Secure cloud storage solutions for large datasets
• Existing data pipelines for image acquisition from field devices or cameras
• User authentication and authorization systems for web interface access

• Model accuracy of at least 90% in image classification tasks
• Scalable cloud-based infrastructure supporting large datasets and high inference throughput
• Data security compliant with industry standards for sensitive customer data
• High availability and reliable uptime to support field operations
• Fast response times (< 2 seconds per inference) for real-time analysis

Implementing this AI-powered visual inspection system is expected to significantly reduce manual verification efforts, decrease inspection time per installation, and improve overall quality assurance accuracy to over 90%. The system will enable quicker deployment, reduce resource costs, and elevate customer satisfaction through consistent and reliable installation validation.