AI-Powered Drone Surveillance & Anomaly Detection
The client needed more control over multi-kilometer pipelines stretching across remote areas. Traditional surveillance was insufficient. They sought an advanced system to detect anomalies like unauthorized people, vehicles, and equipment using drone imagery.
A large international oil and gas corporation responsible for managing and securing extensive pipeline networks. They faced the dauntiing task of monitoring infrastructure that stretches across vast, often remote and hostile environments.
Advanced Computer Vision
Development of custom Convolutional Neural Networks (CNNs) using PyTorch to identify specific entities like people, vehicles, and construction equipment.
Automated Data Pipeline
A robust workflow including image normalization, geometric correction, and filtering to handle variable environmental conditions (snow, lighting).
Scalable Cloud Architecture
Integration with AWS Lambda for serverless processing and S3 for storage, ensuring the system can handle massive datasets from drone fleets.
Environmental Variability
Drone imagery varied wildly in quality due to weather, lighting, and seasons, requiring complex normalization filters.
False Positives
Distinguishing between a legitimate maintenance vehicle and an unauthorized intruder required fine-tuning the model to reduce false alarms.
Remote Scale
Processing data from hundreds of kilometers of pipeline required a highly efficient, automated labeling and training loop.
We engineered an ML-based solution for anomaly detection. Using Label Studio for precise annotation and PyTorch for model training, we built a system that filters, processes, and analyzes drone footage in the cloud. It accurately identifies potential threats while ignoring irrelevant environmental noise.
