{"url":"/dataset/railway-track-misalignment-detection-image","name":"Railway Track Misalignment Detection Image Dataset","full_name":"A Vision-based Solution for Track Misalignment Detection","description_markdown":"The dataset has railway track images of two types: normal and defective\r\n\r\nThe task is to classify a given image into normal or defective. \r\n\r\nThe defective images have misalignment problems: buckling or hogging\r\n\r\nIf you find this dataset useful in your research, please cite the below paper:\r\n\r\n@INPROCEEDINGS{9643106,\r\n\r\n  author={Jerripothula, Koteswar Rao and Ansari, Sharik Ali and Nijhawan, Rahul},\r\n\r\n  booktitle={2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)}, \r\n\r\n  title={A Vision-based Solution for Track Misalignment Detection}, \r\n\r\n  year={2021},\r\n\r\n  pages={271-277},\r\n\r\n  keywords={Graphics;Annotations;Railway accidents;Transfer learning;Buildings;Feature extraction;Rail transportation;railway;transfer learning;VGG;Inception;buckling;hogging;misalignment},\r\n\r\n  doi={10.1109/SIBGRAPI54419.2021.00044}\r\n\r\n}","description_withheld":null,"homepage":"https://sites.google.com/site/koteswarraojerripothula/research-group/track-misalignment-detection-sibgrapi-2021","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Railway Track Image Classification","url":"/task/railway-track-image-classification","datasets_with_task":"/datasets/task/railway-track-image-classification"}],"languages":[],"variants":["Railway Track Misalignment Detection Image Dataset"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/railway-track-image-classification-on-railway","task":"Railway Track Image Classification","dataset_variant":"Railway Track Misalignment Detection Image Dataset","rows":4,"metrics":["Classification Accuracy","Epochs","Learning Rate","Batch Size"],"first_row_in_archive_order":{"model":"VGG16","paper":null,"metrics":{"Batch Size":"128","Classification Accuracy":"0.982","Epochs":"5","Learning Rate":"0.0001"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}