Transmission Tower Equipment Defect Detection

Detection + segmentation · Field robustness · Multiple defect types · Preventive maintenance

Overview

Inspection system for transmission tower defects including cracks, corrosion, loose components, and surface damage. Computer vision pipeline with robust deep learning models for localization and classification of multiple failure modes.

🗼 Infrastructure 🎯 Detection 🔬 Segmentation Python PyTorch 🎯 YOLO OpenCV

Problem

  • Multiple defect types: cracks, corrosion, loose components, surface damage.
  • Challenging field conditions: weather, lighting, viewing angles.
  • Manual inspection dangerous and time-consuming.
  • Need for early detection to prevent catastrophic failures.

Solution

  • Computer vision pipeline for defect detection and classification.
  • Combined object detection + segmentation for precise localization.
  • Data augmentation for lighting/weather robustness.
  • Multi-class detection across various failure modes.

My Role

  • Designed computer vision pipeline for defect detection from images.
  • Developed deep learning models (detection + segmentation).
  • Applied data augmentation for field robustness.
  • Validated across multiple defect types and conditions.

Implementation

Language: Python

Libraries:

PyTorch YOLO OpenCV

Defect Types: Cracks, corrosion, loose components, surface damage

Results Gallery

Tower Equipment Defect Detection

End Result

  • High-accuracy real-time defect detection across multiple failure modes.
  • Improved maintenance scheduling and safety through early detection.
  • Reduced need for dangerous manual tower inspections.