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DISC: A Large-scale Virtual Dataset for Simulating Disaster Scenarios

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DISC: A Large-scale Virtual Dataset for Simulating Disaster ...

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hae-Gon Jeon Sunghoon Im Byeong-Uk Lee Dong-Geol Choi Martial Hebert In So Kweon The Robotics Institute Carnegie Mellon University Pittsburgh PA USA The Robotics and Computer Vision Lab. KAIST Daejeon Republic of Korea Department of Information and Communication Engineering Hanbat National University Daejeon Republic of Korea

In this paper, we present the first large-scale synthetic dataset for visual perception in disaster scenarios, and analyze state-of-the-art methods for multiple computer vision tasks with reference baselines. We simulated before and after disaster scenarios such as fire and building collapse for fifteen different locations in realistic virtual worlds. The dataset consists of more than 300K high-resolution stereo image pairs, all annotated with ground-truth data for semantic segmentation, depth, optical flow, surface normal estimation and camera pose estimation. To create realistic disaster scenes, we manually augmented the effects with 3D models using physical-based graphics tools. We use our dataset to train state-of-the-art methods and evaluate how well these methods can recognize the disaster situations and produce reliable results on virtual scenes as well as real-world images. The results obtained from each task are then used as inputs to the proposed visual odometry network for generating 3D maps of buildings on fire. Finally, we discuss challenges for future research.

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I-BALLAST: computer vision solutions for ballast degradation analysis using deep-learning and data fusion methods

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Geodata and AI

Geodata and AI 2024年 1卷

作者： Kelin Ding Jiayi Luo Haohang Huang John M. Hart Issam I.A. Qamhia Erol Tutumluer Hugh Thompson Theodore R. Sussmann University of Illinois Urbana-Champaign Department of Civil and Environmental Engineering 205 North Mathews Urbana IL 61801 USA Computer Vision and Robotics Laboratory University of Illinois Urbana-Champaign Coordinated Science Laboratory 1308 West Main Street Urbana Illinois 61801 USA Federal Railroad Administration 1200 New Jersey Avenue SE Washington DC 20590 USA U.S. DOT Volpe Center Structures and Dynamics 55 Broadway Cambridge MA 02142 USA

Ballast plays a significant role in supporting tracks under repeated loading. Ballast Scanning Vehicle (BSV) is an automated platform recently developed to acquire high-quality data to assist with ballast inspection and detailed geotechnical analyses using computer vision technology. To process the image data collected by the BSV, this paper introduces an advanced image analysis software for ballast, i.e., I-BALLAST. I-BALLAST encompasses advanced data fusion techniques that integrate multiple data sources, thus producing more consistent, accurate, and comprehensive information. Field data collected by multiple image acquisition devices and other sensors like Global Positioning System (GPS) can be combined for conducting comprehensive analyses, such as segmented particle point clouds generation and varying-window-based Particle Size Distribution (PSD) and Fouling Index (FI) analyses along track sections with aligned GPS coordinates. I-BALLAST has been tested in multiple field inspections, proving it an efficient and innovative tool for ballast fouling condition evaluations.

关键词： Ballast degradation Railroad Field inspection computer vision Deep learning Data fusion

PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration

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arXiv 2020年

作者： Gu, Jinjin Cai, Haoming Chen, Haoyu Ye, Xiaoxing Ren, Jimmy S. Dong, Chao School of Data Science Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SenseTime Research SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Image reconstruction

TTPP: Temporal transformer with progressive prediction for efficient action anticipation

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arXiv 2020年

作者： Wang, Wen Peng, Xiaojiang Su, Yanzhou Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeline is inefficient in capturing long-term information which may limit its performance in predication task. To address this problem, this paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction (TTPP) framework, which repurposes a Transformer-style architecture to aggregate observed features, and then leverages a light-weight network to progressively predict future features and actions. Specifically, predicted features along with predicted probabilities are accumulated into the inputs of subsequent prediction. We evaluate our approach on three action datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also conduct a comprehensive study for several popular aggregation and prediction strategies. Extensive results show that TTPP not only outperforms the state-of-the-art methods but also more efficient. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

Image Restoration for Terahertz Image Based on Complex-Valued Deconvolution 8

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Image Restoration for Terahertz Image Based on Complex-Value...

8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Ning, Wei Qi, Feng Wang, Jinkuan Northeastern University School of Computer Science and Engineering Shenyang110169 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

According to the unique characteristics of terahertz (THz) waves, THz imaging has become a hot topic in widely application areas. However, the imaging resolution is constrained by its long wavelength. Generally, the deconvolution is always used to reconstruct the object function. In this paper, a method that combining the classic deconvolution algorithm with the complex-value data processing is investigated. It is posed that the proposed complex-number deconvolution achieves better performance compared to the real-valued deconvolution. © 2019 IEEE.

关键词： Deconvolution

Self-supervised visual representations for cross-modal retrieval

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arXiv 2019年

作者： Patel, Yash Gomez, Lluis Rusiñol, Marçal Karatzas, Dimosthenis Jawahar, C.V. Robotics Institute Carnegie Mellon University PA United States Computer Vision Center Universitat Autonoma de Barcelona Spain CVIT KCIS IIIT Hyderabad India

Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are usually limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration (global context), and (2) the semantic context of its caption (local context). Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like image classification and object detection, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

Conditional Affordance Learning for Driving in Urban Environments 2

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Conditional Affordance Learning for Driving in Urban Environ...

2nd Conference on Robot Learning, CoRL 2018

作者： Sauer, Axel Savinov, Nikolay Geiger, Andreas Computer Vision and Geometry Group ETH Zürich Switzerland Robotics Science and System Intelligence Technical University of Munich Germany Autonomous Vision Group MPI for Intelligent Systems University of Tübingen Germany

Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation. © CoRL 2018. All rights reserved.

关键词： Autonomous vehicles

Fast perception, planning, and execution for a robotic butler: Wheeled humanoid m-hubo

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arXiv 2020年

作者： Lee, Moonyoung Heo, Yujin Park, Jinyong Yang, Hyun-Dae Jang, Ho-Deok Benz, Philipp Park, Hyunsub Kweon, In So Oh, Jun-Ho Humanoid Robot Research Center Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Robotics and Computer Vision Laboratory Department of Electrical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of

As the aging population grows at a rapid rate, there is an ever growing need for service robot platforms that can provide daily assistance at practical speed with reliable performance. In order to assist with daily tasks such as fetching a beverage, a service robot must be able to perceive its environment and generate corresponding motion trajectories. This becomes a challenging and computationally complex problem when the environment is unknown and thus the path planner must sample numerous trajectories that often are sub-optimal, extending the execution time. To address this issue, we propose a unique strategy of integrating a 3D object detection pipeline with a kinematically optimal manipulation planner to significantly increase speed performance at runtime. In addition, we develop a new robotic butler system for a wheeled humanoid that is capable of fetching requested objects at 24% of the speed a human needs to fulfill the same task. The proposed system was evaluated and demonstrated in a real-world environment setup as well as in public exhibition. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

Acute and sub-acute stroke lesion segmentation from multimodal MRI

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arXiv 2018年

作者： Clèrigues, Albert Valverde, Sergi Bernal, Jose Freixenet, Jordi Oliver, Arnau Lladó, Xavier Institute of Computer Vision and Robotics University of Girona Spain

Background and objective. Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctorsmake better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. Methods. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a UNet based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing. Results. The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance. Conclusions. Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of clas

关键词： Magnetic resonance imaging