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IEEE International Conference on robotics and Automation (ICRA)

作者： Haram Kim Pyojin Kim H. Jin Kim Lab for Autonomous Robotics Research (LARR) Seoul National University Seoul South Korea Computer Graphics and Vision Lab (GrUVi) Simon Fraser University Burnaby BC

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Detection of moving objects is an essential capability in dealing with dynamic environments. Most moving object detection algorithms have been designed for color images without depth. For robotic navigation where real-time RGBD data is often readily available, utilization of the depth information would be beneficial for obstacle recognition. Here, we propose a simple moving object detection algorithm that uses RGB-D images. The proposed algorithm does not require estimating a background model. Instead, it uses an occlusion model which enables us to estimate the camera pose on a background confused with moving objects that dominate the scene. The proposed algorithm allows to separate the moving object detection and visual odometry (VO) so that an arbitrary robust VO method can be employed in a dynamic situation with a combination of moving object detection, whereas other VO algorithms for a dynamic environment are inseparable. In this paper, we use dense visual odometry (DVO) as a VO method with a bi-square regression weight. Experimental results show the segmentation accuracy and the performance improvement of DVO in the situations. We validate our algorithm in public datasets and our dataset which also publicly accessible.

关键词： Cameras Heuristic algorithms Object detection Robustness Trajectory Visual odometry Feature extraction

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DCGANs for realistic breast mass augmentation in X-Ray mammography

arXiv

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arXiv 2019年

作者： Alyafi, Basel Diaz, Oliver Marti, Robert Computer Vision and Robotics Institute University of Girona Sabadell Spain

Early detection of breast cancer has a major contribution to curability, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant CADe tool currently, has played a great role in object detection in computer vision, but it suffers from a limiting property: the need of a large amount of labelled data. This becomes stricter when it comes to medical datasets which require high-cost and time-consuming annotations. Furthermore, medical datasets are usually imbalanced, a condition that often hinders classifiers performance. The aim of this paper is to learn the distribution of the minority class to synthesise new samples in order to improve lesion detection in mammography. Deep Convolutional Generative Adversarial Networks (DCGANs) can efficiently generate breast masses. They are trained on increasing-size subsets of one mammographic dataset and used to generate diverse and realistic breast masses. The effect of including the generated images and/or applying horizontal and vertical flipping is tested in an environment where a 1:10 imbalanced dataset of masses and normal tissue patches is classified by a fully-convolutional network. A maximum of ∼ 0.09 improvement of F1 score is reported by using DCGANs along with flipping augmentation over using the original images. We show that DCGANs can be used for synthesising photo-realistic breast mass patches with a considerable diversity. It is demonstrated that appending synthetic images in this environment, along with flipping, outperforms the traditional augmentation method of flipping solely, offering faster improvements as a function of the training set *** Codes 68U10 (Primary) 68U20 (Secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Mammography

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Self-slimmed vision Transformer

arXiv

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arXiv 2021年

作者： Zong, Zhuofan Li, Kunchang Song, Guanglu Wang, Yali Qiao, Yu Leng, Biao Liu, Yu School of Computer Science and Engineering Beihang University China SenseTime Research China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Shanghai AI Laboratory China

vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping insignificant tokens to reduce the computational cost of ViTs. But when the dropping ratio increases, this hard manner will inevitably discard the vital tokens, which limits its efficiency. To solve the issue, we propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation. As a general method of token hard dropping, our TSM softly integrates redundant tokens into fewer informative ones. It can dynamically zoom visual attention without cutting off discriminative token relations in the images, even with a high slimming ratio. Furthermore, we introduce a concise Feature Recalibration Distillation (FRD) framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the unstructured token in a flexible auto-encoder manner. Due to the similar structure between teacher and student, our FRD can effectively leverage structure knowledge for better convergence. Finally, we conduct extensive experiments to evaluate our SiT. It demonstrates that our method can speed up ViTs by 1.7× with negligible accuracy drop, and even speed up ViTs by 3.6× while maintaining 97% of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet. Code is available at https://***/Sense-X/SiT. © 2021, CC BY.

关键词： Distillation

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RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving 16th

RTM3D: Real-Time Monocular 3D Detection from Object Keypoin...

引用

16th European Conference on computer vision, ECCV 2020

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision ShenyangLiaoning China

ISBN: (纸本)9783030585792

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. © 2020, Springer Nature Switzerland AG.

关键词： Real time systems

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Multi-camera Torso Pose Estimation using Graph Neural Networks

arXiv

引用

arXiv 2020年

作者： Rodriguez-Criado, Daniel Bachiller, Pilar Bustos, Pablo Vogiatzis, George Manso, Luis J. School of Engineering and Applied Science Computer Science Department Aston University United Kingdom Robotics and Artificial Vision Laboratory Caceres School of Technology Universidad de Extremadura Spain

Estimating the location and orientation of humans is an essential skill for service and assistive robots. To achieve a reliable estimation in a wide area such as an apartment, multiple RGBD cameras are frequently used. Firstly, these setups are relatively expensive. Secondly, they seldom perform an effective data fusion using the multiple camera sources at an early stage of the processing pipeline. Occlusions and partial views make this second point very relevant in these scenarios. The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125mm for the location and 10◦ for the orientation using low-resolution RGB images. The experiments, conducted in an apartment with three cameras, benchmarked two different graph neural network implementations and a third architecture based on fully connected layers. The software used has been released as open-source in a public repository1. Copyright © 2020, The Authors. All rights reserved.

关键词： Data fusion

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Automation of Predictive Maintenance: An Experimental Framework for Aircraft Landing Gear

Engineering Reports

引用

Engineering Reports 2025年第6期7卷

作者： Izaak Stanton Kamran Munir Ahsan Ikram Murad El-Bakry Centre for Machine Vision Bristol Robotics Laboratory (BRL) University of the West of England (UWE) Bristol UK Computer Science Research Centre (CSRC) School of Computing and Creative Technologies (SCC) College of Arts Technology and Environment (CATE) University of the West of England (UWE) Bristol UK Airbus Operations Ltd. Filton Bristol UK

Terabytes of data are recorded per flight by modern aircraft, providing a goldmine for predictive maintenance modeling, however, the required domain knowledge to build ML tools limits the number developed by airline manufacturers each year. Automated machine learning (AutoML) libraries can simplify model development, providing features such as automated preprocessing, model selection, and hyperparameter tuning to improve the efficiency and accessibility of the development workflow. This research presents an experimental analysis comparing industry-selected machine learning models and a hand-picked selection of automated machine-learning tools. The selected models were evaluated against real and synthetic time series datasets for different Airbus landing gear components across six datasets. The traditional and automated models obtained comparable MAE and F1 scores on regression and classification problems, accordingly, demonstrating the effectiveness of their use in this field. Based on these findings, a robust framework is proposed to utilize automated ML to optimize predictive maintenance tool development. This research is a stepping stone towards greater use of automation for predictive maintenance and presents insights into the field and AutoML. By integrating greater automation, AutoML can exploit more of the available data and deskill the development process to enable non-data scientists to produce health monitoring models for a more diverse pool of aircraft components.

关键词： aircraft maintenance automated machine learning machine learning predictive maintenance

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

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16th European Conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The 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 Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

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. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Bebop 2 Quadrotor as a Platform for Research and Education in robotics and Control Engineering

Bebop 2 Quadrotor as a Platform for Research and Education i...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Wojciech Giernacki Piotr Kozierski Jacek Michalski Marek Retinger Rafal Madonski Pascual Campoy Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering of Poznan University of Technology Poznań Poland Institute of Computing Science (Faculty of Computing and Telecommunications) and with the Institute of Robotics and Machine Intelligence (Faculty of Control Robotics and Electrical Engineering) Poznan University of Technology Poznań Poland International Energy College Energy Electricity Research Center Jinan University Zhuhai China Computer Vision and Aerial Robotics (CVAR) Centre for Automation and Robotics Technical University of Madrid (UPM) Madrid Spain

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In conducting research and teaching in fields related to unmanned aerial vehicles (UAVs), it is particularly important to select a universal, safe, open research platform and tools for rapid prototyping. Ready-to-use, low-cost micro-class UAVs such as Bebop 2 are successfully used in that regard. This article presents how to use the potential of this flying robot with Robot Operating System (ROS). The most important software solutions for the developed experimental testbed FlyBebop are characterized here. Their capabilities in research and education are exemplified using three distinct cases: 1) research results on the method of optimal, in-flight, iterative self-tuning of UAV position controller parameters (based only on current measurements), 2) the use of the reinforcement learning method in the autonomous landing of a single drone on a moving vehicle, 3) planning the movement of UAVs for autonomous video recording along the planned path in the arrangement: "cameraman drone" and "lighting technician drones".

关键词： Drones Robots Education Cameras Sensors Tools

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Semantic SLAM for an AUV using object recognition from point clouds

引用

IFAC-PapersOnLine 2018年第29期51卷 360-365页

作者： Himri, K. Ridao, P. Gracias, N. Palomer, A. Palomeras, N. Pi, R. Computer Vision and Robotics Institute Universitat de Girona Pic de Peguera 13 Girona17003 Spain

This paper presents a navigation and mapping system for an autonomous underwater vehicle (AUV) while operating near a man-made underwater environment. The objective is to recognize objects (or object parts) and use these as landmarks for simultaneous localization and mapping (SLAM). This approach is intended as the first step towards autonomous object manipulation, to be carried out at a later stage. The approach contains two main components: Object recognition from range data, and feature-based semantic SLAM. For the first component we propose an automatic method for the recognition and location of 3D objects using 3D point clouds as input, extracted from a laser scanner. Since it is common in inspection maintenance and repair (IMR) applications to have access to the 3D models of the objects of interest, the proposed method assumes a priori knowledge of the 3D models of these objects. In typical man-made environments, such objects can be distinct components of a structure, such as valves, pipes and wet-mateable connectors. The object recognition is based on recently proposed global descriptors for point clouds, that allow a compact description of the object shape, which is independent of the object view point. Once an object is recognized, its pose with respect to the AUV is determined using an ICP-based method. The second component of the approach is a feature based SLAM algorithm that uses the recognized objects as landmarks to improve the AUV navigation. The paper presents preliminary results obtained with the Girona 500 AUV, equipped with a fast laser scanner recently developed at the University of Girona. Tests conducted in a controlled environment (water tank) illustrate the suitability of the approach. © 2018

关键词： Autonomous underwater vehicles Air navigation Content based retrieval Laser applications Mapping Object recognition Repair robotics Scanning Semantics Three dimensional computer graphics Water tanks 3d object recognition Autonomous underwater vehicles (AUVs) Descriptors Laser scanner Mapping (SLAM) Shape functions Simultaneous localizations Viewpoint Features Histogram (VFH)

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