AI-Based Automated QA for Solar Rooftop Inspection

Background:

Largest Indian Company in Solar Installation Space

Objective : 

Largest Indian Company in Solar Installation Space, one of India's largest integrated power companies, aimed to enhance its solar rooftop inspection process using AI-based automation. With thousands of solar panel installations across various locations, the company sought a solution that could efficiently detect defects, verify structural integrity, and ensure compliance with safety regulations.

To achieve this, Largest Indian Company in Solar Installation Space partnered with our AI experts to develop an advanced quality assurance (QA) system using deep learning and computer vision models. The solution leveraged YOLOv5x6 for defect detection and Amazon SageMaker for model training and deployment, enabling real-time, automated inspections.

Challenges Faced:

1. Manual Inspection Inefficiencies – Traditional inspection methods relied on human inspectors, leading to inconsistencies and longer processing times.
2. Scalability Issues – With increasing solar installations, scaling manual inspection efforts was becoming resource-intensive and unsustainable.
3. Defect Detection Accuracy – Identifying subtle defects such as micro-cracks, misalignment, and wiring issues required an AI-driven approach for precision.
4. Integration with Existing Workflows – The AI model needed to seamlessly integrate with Largest Indian Company in Solar Installation Space’s field inspection tools and monitoring systems.

Solution Summary:

Our AI-powered QA system for Largest Indian Company in Solar Installation Space was designed to address these challenges through an automated inspection workflow:

• Model Selection & Development : After evaluating multiple CNN models, YOLOv5x6 was selected for its high accuracy in detecting solar panel defects. The model was trained using Largest Indian Company in Solar Installation Space’s extensive dataset of solar panel images.
• Cloud-Based AI Deployment : Model development was performed on Amazon SageMaker Notebooks, allowing iterative training and fine-tuning. The trained model was deployed via Amazon SageMaker Serverless Endpoints for efficient, real-time inference.
• Automated Defect Detection :  The AI system was trained to identify and classify defects such as: Panel misalignment, Module damage, Wiring defects, Shadowing issues affecting efficiency
• Seamless Integration : The AI model was integrated with Largest Indian Company in Solar Installation Space’s inspection dashboard and field tools, enabling engineers to receive instant insights on defect detection.

Implementation Process

• Data Collection & Preprocessing – Gathering high-resolution images of installed solar panels and annotating defect types.
• Model Training & Evaluation – Training YOLOv5x6 on Largest Indian Company in Solar Installation Space’s dataset and validating performance metrics (achieved mAP of 85%+).
• Deployment & Integration – Deploying the model on Amazon SageMaker and integrating with Largest Indian Company in Solar Installation Space’s field inspection apps.
• Monitoring & Continuous Improvement – Utilizing Amazon CloudWatch for real-time performance monitoring and periodic model retraining to improve accuracy.

Tech Stack:

• AWS Services : AWS SageMaker, Amazon CloudWatch, AWS EC2, AWS Lambda