LineCheck
DeployingAI-powered computer vision system detecting damaged electrical insulators in power transmission lines with 99.38% accuracy
Overview
Power transmission infrastructure requires constant monitoring to prevent outages and ensure grid reliability. Traditional manual inspections are time-consuming, expensive, and often miss critical damage that could lead to failures. LineCheck automates this process using computer vision to detect damaged insulators with near-perfect accuracy.
Built with FastAI and PyTorch, the system uses a fine-tuned ResNet-34 architecture trained on thousands of power infrastructure images. The model can identify various types of insulator damage including cracks, contamination, and structural failures that human inspectors might miss.
Preview
Tech Stack
PythonPyTorchFastAIResNet-34FastAPIDocker
Key Features
99.38% accuracy in detecting damaged electrical insulators
ResNet-34 deep learning model trained on power infrastructure images
FastAPI backend for real-time image processing and analysis
Docker containerization for scalable deployment in industrial environments