train track defects detection and complaint redressal system

Project Code :TCMAPY1486

Objective

The primary objective of this project is to create an automated train track crack classification system that accurately distinguishes between Defective and Non-Defective railway tracks. By leveraging deep learning algorithms, including YOLOv7 and YOLOv8, the system aims to improve the efficiency and accuracy of railway track inspection processes.

Abstract

TRAIN TRACK CRACK CLASSIFICATION

Abstract

The project focuses on train track crack classification for the detection of defective and non-defective railway tracks using deep learning algorithms. The system classifies railway track images into two categories: Defective and Non-Defective. The dataset used for this task is the Railway Track Fault Detection Dataset from Kaggle, which includes images of train tracks with and without cracks. The proposed system leverages advanced machine learning algorithms like YOLOv7 and YOLOv8 to automatically classify these tracks based on their condition. The system's backend is implemented in Python, while the front-end uses HTML, CSS, and JavaScript for user interaction and display. The primary goal is to enhance the efficiency and safety of railway operations by automating the detection of track faults, reducing human error, and providing timely maintenance alerts. With 50 hours of development time allocated, this project aims to create a robust and scalable fault detection system.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.