Check out my article on Railway Track Detection on Medium:
Railway 🚆 Track Detection
This repository contains code and resources for railway track detection using various computer vision techniques. Methodologies
The project explores several different approaches for detecting railway tracks in images and videos: ● Hough Transform: This classical computer vision technique identifies lines within images by transforming them into a parameter space where lines are represented as points. By detecting clusters of points in this parameter space, lines in the original image can be identified. This method is particularly well-suited for detecting straight lines, such as railway tracks.
● Hough Transform with Morphological Preprocessing: Morphological operations, such as erosion and dilation, can be used to enhance the edges of the railway tracks and remove noise before applying the Hough Transform. This can improve the accuracy and robustness of the line detection process. ● Histogram Equalization: This technique adjusts the contrast of the image by distributing pixel intensities more evenly across the histogram. This can improve the visibility of the railway tracks and enhance the performance of subsequent processing steps.
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Hough Transform with Masking, ROI Definition, and DBSCAN Clustering: This advanced methodology combines the Hough Transform with several techniques to improve accuracy and robustness. A mask is used to define a region of interest (ROI) where the railway tracks are likely to be located, reducing the computational burden and minimizing the impact of noise from other parts of the image. DBSCAN clustering is then applied to group detected lines into distinct tracks, enabling accurate track counting.
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YOLO: You Only Look Once (YOLO) is a state-of-the-art object detection algorithm that can be trained to detect railway tracks as objects within images or videos. YOLO models are known for their speed and efficiency, making them suitable for real-time applications.
Code
The repository includes code implementations for several of these methodologies, including:
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Hough Transform with masking & defining region of interest & using DBSCAN Clustering algorithm:
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Railway Track Detection using Hough Transform:
● Railway Track Detection using YOLO: Snippets of code showing how to train and use a YOLO model. Each code file includes instructions on how to run the code and the required dependencies. Data The project uses a dataset of images and videos of railway tracks. The data is not included in the repository but can be obtained from publicly available sources or by collecting your own data. Results The results of the different methodologies are presented in the form of images and videos with the detected railway tracks highlighted. The sources do not discuss quantitative results. Conclusion This project demonstrates the effectiveness of various computer vision techniques for railway track detection. The best-performing method will depend on the specific application and the characteristics of the data.