Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform

• URL: http://arxiv.org/abs/2001.11175v2
• Date: Mon, 3 Feb 2020 04:27:32 GMT
• Title: Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
• Authors: Jongmin Yu, Duyong Kim, Younkwan Lee, and Moongu Jeon
• Abstract summary: We propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT) AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not need annotations for road pavement defects.
• Score: 8.644679871804872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements on various studies on computer vision and deep learning. Although a large-scale and well-annotated datasets enhance the performance of detecting road pavement defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT). AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not need annotations for road pavement defects. We evaluate the efficiency of AIFT using GAPs384 dataset, Cracktree200 dataset, CRACK500 dataset, and CFD dataset. The experimental results demonstrate that the proposed approach detects various road detects, and it outperforms existing state-of-the-art approaches.

Related papers

• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Spatio-Temporal Context Modeling for Road Obstacle Detection [12.464149169670735]
  A data-driven context-temporal model of the driving scene is constructed with the layouts of the training data. Obstacles are detected via state-of-the-art object detection algorithms, and the results are combined with the generated scene.
  arXiv  Detail & Related papers  (2023-01-19T07:06:35Z)
• Perspective Aware Road Obstacle Detection [104.57322421897769]
  We show that road obstacle detection techniques ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. We leverage this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening.
  arXiv  Detail & Related papers  (2022-10-04T17:48:42Z)
• Road Rutting Detection using Deep Learning on Images [0.0]
  Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. This paper proposes a novel road rutting dataset comprising of 949 images and provides both object level and pixel level annotations. Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset.
  arXiv  Detail & Related papers  (2022-09-28T16:53:05Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• An Efficient Approach for Anomaly Detection in Traffic Videos [30.83924581439373]
  We propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices. The proposed approach comprises a pre-processing module that detects changes in the scene and removes the corrupted frames. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
  arXiv  Detail & Related papers  (2021-04-20T04:43:18Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Anomalous Motion Detection on Highway Using Deep Learning [14.617786106427834]
  This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods.
  arXiv  Detail & Related papers  (2020-06-15T05:40:11Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

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.