UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite

• URL: http://arxiv.org/abs/2304.08842v3
• Date: Mon, 1 Jan 2024 13:56:49 GMT
• Title: UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
• Authors: Sicen Guo, Jiahang Li, Yi Feng, Dacheng Zhou, Denghuang Zhang, Chen Chen, Shuai Su, Xingyi Zhu, Qijun Chen, Rui Fan
• Abstract summary: We introduce the road pothole detection task, the first online competition published within this benchmark suite. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential.
• Score: 21.565438268381467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain.

Related papers

• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches [4.516330345599765]
  High-quality images are crucial in remote sensing and UAV applications. atmospheric haze can severely degrade image quality, making image dehazing a critical research area.
  arXiv  Detail & Related papers  (2024-05-13T07:35:24Z)
• Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior [9.85003064364004]
  Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. We propose a comprehensive end-to-end multimodal deep learning framework designed for simultaneously detecting accurate AOI boundaries and validating the reliability of AOI.
  arXiv  Detail & Related papers  (2024-01-12T12:54:30Z)
• AI Powered Road Network Prediction with Multi-Modal Data [0.9426448361599084]
  This study presents an innovative approach for automatic road detection with deep learning, by employing fusion strategies for utilizing both lower-resolution satellite imagery and GPS trajectory data. We rigorously investigate both early and late fusion strategies, and assess deep learning based road detection performance using different fusion settings.
  arXiv  Detail & Related papers  (2023-12-28T14:31:07Z)
• Unified Domain Adaptive Semantic Segmentation [96.74199626935294]
  Unsupervised Adaptive Domain Semantic (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain. We propose a Quad-directional Mixup (QuadMix) method, characterized by tackling distinct point attributes and feature inconsistencies. Our method outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T09:18:49Z)
• Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via Automatic Label Generation [4.921246328739616]
  This research work seeks to explore and identify strategies that can determine road topology information in 2D and 3D under highly dynamic urban driving scenarios. To facilitate this exploration, we introduce a substantial dataset comprising nearly one million automatically labeled data frames.
  arXiv  Detail & Related papers  (2023-11-03T17:20:34Z)
• Deep Industrial Image Anomaly Detection: A Survey [85.44223757234671]
  Recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD) In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques. We highlight several opening challenges for image anomaly detection.
  arXiv  Detail & Related papers  (2023-01-27T03:18:09Z)
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline [95.88825497452716]
  Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. GREW is the first large-scale dataset for gait recognition in the wild. SPOSGait is the first NAS-based gait recognition model.
  arXiv  Detail & Related papers  (2022-05-05T14:57:39Z)
• MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection [80.83725644958633]
  Current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. We present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation.
  arXiv  Detail & Related papers  (2021-09-15T14:11:53Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• On the Challenges of Open World Recognitionunder Shifting Visual Domains [23.999211737485812]
  This work investigates whether Open World Recognition (OWR) algorithms are effective under domain-shift. OWR has the goal to produce systems capable of breaking the semantic limits present in the initial training set. Our analysis shows that this degradation is only slightly mitigated by coupling OWR with domain generalization techniques.
  arXiv  Detail & Related papers  (2021-07-09T14:25:45Z)

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.