Rail-5k: a Real-World Dataset for Rail Surface Defects Detection

• URL: http://arxiv.org/abs/2106.14366v1
• Date: Mon, 28 Jun 2021 01:53:52 GMT
• Title: Rail-5k: a Real-World Dataset for Rail Surface Defects Detection
• Authors: Zihao Zhang, Shaozuo Yu, Siwei Yang, Yu Zhou, Bingchen Zhao
• Abstract summary: This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario. We collected over 5k high-quality images from railways across China, and annotated 1100 images with the help from railway experts to identify the most common 13 types of rail defects.
• Score: 10.387206647221626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task. We collected over 5k high-quality images from railways across China, and annotated 1100 images with the help from railway experts to identify the most common 13 types of rail defects. The dataset can be used for two settings both with unique challenges, the first is the fully-supervised setting using the 1k+ labeled images for training, fine-grained nature and long-tailed distribution of defect classes makes it hard for visual algorithms to tackle. The second is the semi-supervised learning setting facilitated by the 4k unlabeled images, these 4k images are uncurated containing possible image corruptions and domain shift with the labeled images, which can not be easily tackle by previous semi-supervised learning methods. We believe our dataset could be a valuable benchmark for evaluating robustness and reliability of visual algorithms.

Related papers

• ForgeryTTT: Zero-Shot Image Manipulation Localization with Test-Time Training [42.58645429356456]
  Social media is increasingly plagued by realistic fake images, making it hard to trust content. Previous algorithms to detect these fakes often fail in new, real-world scenarios because they are trained on specific datasets. We introduce ForgeryTTT, the first method leveraging test-time training to identify manipulated regions in images.
  arXiv  Detail & Related papers  (2024-10-05T04:41:55Z)
• Enhancing Low-Light Images in Real World via Cross-Image Disentanglement [58.754943762945864]
  We propose a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions. Our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.
  arXiv  Detail & Related papers  (2022-01-10T03:12:52Z)
• Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation [43.03533959429743]
  We propose OTTER, which uses online optimal transport to find a soft image-text match as labels for contrastive learning. Based on pretrained image and text encoders, models trained with OTTER achieve strong performance with only 3M image text pairs.
  arXiv  Detail & Related papers  (2021-12-17T11:27:26Z)
• A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model [2.1473182295633224]
  This dataset contains images collected from various sources for three different disasters: fire, water and land. There are 13,720 manually annotated images in this dataset, each image is annotated by three individuals. A three layer attention model (TLAM) is trained and average five fold validation accuracy of 95.88% is achieved.
  arXiv  Detail & Related papers  (2021-07-02T21:18:20Z)
• Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images [79.34600869202373]
  We show that inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations. Our method yields impressive results, outperforming the previous best unsupervised methods by 7.74% and 9.24%.
  arXiv  Detail & Related papers  (2021-05-25T12:22:11Z)
• DetCo: Unsupervised Contrastive Learning for Object Detection [64.22416613061888]
  Unsupervised contrastive learning achieves great success in learning image representations with CNN. We present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches. DetCo consistently outperforms supervised method by 1.6/1.2/1.0 AP on Mask RCNN-C4/FPN/RetinaNet with 1x schedule.
  arXiv  Detail & Related papers  (2021-02-09T12:47:20Z)
• Grafit: Learning fine-grained image representations with coarse labels [114.17782143848315]
  This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
  arXiv  Detail & Related papers  (2020-11-25T19:06:26Z)
• Semi-Supervised Image Deraining using Gaussian Processes [18.434430658837258]
  We propose a semi-supervised learning framework which enables the network in learning to derain using synthetic dataset. We show that the proposed method is able to effectively leverage unlabeled data thereby resulting in significantly better performance as compared to labeled-only training.
  arXiv  Detail & Related papers  (2020-09-25T17:16:16Z)
• Deep Traffic Sign Detection and Recognition Without Target Domain Real Images [52.079665469286496]
  We propose a novel database generation method that requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one.
  arXiv  Detail & Related papers  (2020-07-30T21:06:47Z)
• Syn2Real Transfer Learning for Image Deraining using Gaussian Processes [92.15895515035795]
  CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. Due to challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset.
  arXiv  Detail & Related papers  (2020-06-10T00:33:18Z)
• Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks [3.441021278275805]
  Weather is an important factor affecting transportation and road safety. We leverage state-of-the-art convolutional neural networks in labelling images taken by street and highway cameras across North America.
  arXiv  Detail & Related papers  (2020-01-27T18:07:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.