This research-based computer vision system automates the digital transcription of analogue gauge readings from video input. By pairing deep learning keypoint detection with geometric perspective correction, the system accurately reads dial values in real time and streams logged metrics directly to cloud-based monitoring tools.

Key Technical Contributions & Features

1. YOLOv8 Keypoint Detection Pipeline: Trained a YOLOv8-pose/keypoint model to precisely identify four critical reference coordinates on the gauge dial—the scale minimum point, scale maximum point, pointer center, and pointer tip. Achieved 93% mAP across a hybrid dataset of 10,000 synthetic gauge images and 2,700 annotated real-world video frames.

2. Parallax & Geometric Distortion Correction: Designed a custom geometric mapping algorithm to overcome 3D parallax errors—where non-coplanar pointers and scale faces skew readings from off-center camera angles. The algorithm computes angular sweeps and normalizes scale orientation for precise angular-to-value conversion.

3. Real-Time Video Analytics Backend: Built an automated video processing pipeline using OpenCV and NumPy to capture live feeds, track pointer displacement continuously, and transcribe visual metrics into structured digital numbers.

4. Automated Cloud Logging: Integrated real-time data exporters to continuously push transcribed gauge values to cloud spreadsheets (Google Sheets / Excel) for continuous tracking and telemetry monitoring. Tech Stack

Languages & Core: Python, NumPy

Computer Vision & Image Processing: OpenCV

Deep Learning & Machine Learning: YOLOv8 (Keypoint Detection), Convolutional Neural Networks (CNN), Scikit-Learn

Data Integration: Google Sheets API