Pipeline Inspection Robot with Machine Learning Using Camera

Abstract

Pipeline inspection is a critical process for ensuring the structural integrity and operational efficiency of pipelines used in industries such as oil and gas, water distribution, and sewage management. This project presents a Bluetooth-controlled pipeline inspection robot equipped with a NodeMCU microcontroller and a camera for real-time video capture. The robot is designed for remote navigation through pipelines, allowing operators to inspect confined and hazardous areas efficiently. The captured video footage is processed using MATLAB and analyzed through machine learning algorithms to detect structural anomalies, including cracks, leaks, blockages, and foreign objects. For accurate defect detection, a Convolutional Neural Network is employed to analyze video frames and classify pipeline conditions. The model is trained on a pipeline image dataset to accurately detect defects like cracks, leaks, and blockages. Anomaly detection algorithms such as SVM or XGBoost further enhance the classification and flagging of irregularities with high precision. The system ensures proactive maintenance, reducing potential failures and improving pipeline lifespan. By integrating IoT-based control with AI-driven inspection, this robotic system enhances safety, efficiency, and cost-effectiveness in pipeline monitoring. The proposed solution aims to revolutionize infrastructure maintenance by providing an automated, real-time, and intelligent inspection approach.