The 1st Data Science for Pavements Challenge

• URL: http://arxiv.org/abs/2206.04874v1
• Date: Fri, 10 Jun 2022 05:02:31 GMT
• Title: The 1st Data Science for Pavements Challenge
• Authors: Ashkan Behzadian, Tanner Wambui Muturi, Tianjie Zhang, Hongmin Kim, Amanda Mullins, Yang Lu, Neema Jasika Owor, Yaw Adu-Gyamfi, William Buttlar, Majidifard Hamed, Armstrong Aboah, David Mensching, Spragg Robert, Matthew Corrigan, Jack Youtchef, Dave Eshan
• Abstract summary: The Data Science for Pavement Challenge (DSPC) seeks to accelerate the research and development of automated vision systems for pavement condition monitoring and evaluation. The first edition of the competition attracted 22 teams from 8 countries. The paper summarizes the solutions from the top 5 teams.
• Score: 5.610512429240221
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Data Science for Pavement Challenge (DSPC) seeks to accelerate the research and development of automated vision systems for pavement condition monitoring and evaluation by providing a platform with benchmarked datasets and codes for teams to innovate and develop machine learning algorithms that are practice-ready for use by industry. The first edition of the competition attracted 22 teams from 8 countries. Participants were required to automatically detect and classify different types of pavement distresses present in images captured from multiple sources, and under different conditions. The competition was data-centric: teams were tasked to increase the accuracy of a predefined model architecture by utilizing various data modification methods such as cleaning, labeling and augmentation. A real-time, online evaluation system was developed to rank teams based on the F1 score. Leaderboard results showed the promise and challenges of machine for advancing automation in pavement monitoring and evaluation. This paper summarizes the solutions from the top 5 teams. These teams proposed innovations in the areas of data cleaning, annotation, augmentation, and detection parameter tuning. The F1 score for the top-ranked team was approximately 0.9. The paper concludes with a review of different experiments that worked well for the current challenge and those that did not yield any significant improvement in model accuracy.

Related papers

• SDFR: Synthetic Data for Face Recognition Competition [51.9134406629509]
  Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
  arXiv  Detail & Related papers  (2024-04-06T10:30:31Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Dialogue-Contextualized Re-ranking for Medical History-Taking [5.039849340960835]
  We present a two-stage re-ranking approach that helps close the training-inference gap by re-ranking the first-stage question candidates. We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone.
  arXiv  Detail & Related papers  (2023-04-04T17:31:32Z)
• Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 [112.36155380260655]
  This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series. Results show that DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. A high level modular organization emerged featuring a "meta-learner", "data ingestor", "model selector", "model/learner", and "evaluator"
  arXiv  Detail & Related papers  (2022-01-11T06:21:18Z)
• AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge [21.49840594645196]
  First Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020. We present its design, analysis, and post-hoc experiments.
  arXiv  Detail & Related papers  (2021-07-28T06:30:46Z)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)
• An Empirical Study of Vehicle Re-Identification on the AI City Challenge [19.13038665501964]
  The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition.
  arXiv  Detail & Related papers  (2021-05-20T12:20:52Z)
• The 5th AI City Challenge [51.83023045451549]
  The fifth AI City Challenge attracted 305 participating teams across 38 countries. The evaluation was conducted on both algorithmic effectiveness and computational efficiency. Results show the promise of AI in Smarter Transportation.
  arXiv  Detail & Related papers  (2021-04-25T19:15:27Z)
• Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis [2.752817022620644]
  This study deploys state-of-the-art deep learning algorithms to detect and characterize pavement distresses. The models were trained using 21,041 images captured across urban and rural streets of Japan, Czech Republic and India. The best performing model achieved an F1 score of 0.58 and 0.57 on two test datasets released by the IEEE Global Road Damage Detection Challenge.
  arXiv  Detail & Related papers  (2020-10-21T00:26:59Z)
• LID 2020: The Learning from Imperfect Data Challenge Results [242.86700551532272]
  Learning from Imperfect Data workshop aims to inspire and facilitate the research in developing novel approaches. We organize three challenges to find the state-of-the-art approaches in weakly supervised learning setting. This technical report summarizes the highlights from the challenge.
  arXiv  Detail & Related papers  (2020-10-17T13:06:12Z)
• Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims [3.7543966923106438]
  We introduce the first adversarially-regularized, transformer-based claim spotter model. We obtain a 4.70 point F1-score improvement over current state-of-the-art models. We propose a method to apply adversarial training to transformer models.
  arXiv  Detail & Related papers  (2020-02-18T16:51:05Z)

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.