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作者： Michele Grimaldi Carlo Cernicchiaro George Rossides Angelos Ktoris Elias Yfantis Ioannis Kyriakides Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University Marine Robotics Innovation Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Computational Cognition Lab Open University of Cyprus Nicosia Cyprus Marine & Offshore Science Technology & Engineering Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Department of Engineering University of Nicosia Nicosia Cyprus Marine Technology Division Cyprus Marine and Maritime Institute Larnaca Cyprus

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

In response to stringent emission reduction targets imposed by the International Maritime Organization (IMO) and the European Green Deal's Fit for 55 legislation package, the maritime industry has shifted its focus towards decarbonization. While significant attention has been placed on vessels exceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping, amounting to approximately 13% of global shipping transportation and 15% within the European Union (EU), have not received adequate consideration. This abstract introduces the Zero Emission Sea Transporter (ZEST) project, designed to address this issue by developing a zero-emissions multi-purpose catamaran for short sea routes, shown in Figure 1.

关键词： Visualization Scalability Europe Sea measurements Seaports Low carbon economy Digital twins

Machine Learning and computer vision Techniques in Continuous Beehive Monitoring Applications: A Survey

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： computer vision

AO2-DETR: Arbitrary-Oriented Object Detection Transformer

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arXiv 2022年

作者： Dai, Linhui Liu, Hong Tang, Hao Wu, Zhiwei Song, Pinhao The Key Laboratory of Machine Perception Shenzhen Graduate School Peking University Beijing100871 China The Computer Vision Lab ETH Zurich Zurich Switzerland The School of Software Engineering South China University of Technology Guangdong Guangzhou China

Arbitrary-oriented object detection (AOOD) is a challenging task to detect objects in the wild with arbitrary orientations and cluttered arrangements. Existing approaches are mainly based on anchor-based boxes or dense points, which rely on complicated hand-designed processing steps and inductive bias, such as anchor generation, transformation, and non-maximum suppression reasoning. Recently, the emerging transformer-based approaches view object detection as a direct set prediction problem that effectively removes the need for handdesigned components and inductive biases. In this paper, we propose an Arbitrary-Oriented Object DEtection TRansformer framework, termed AO2-DETR, which comprises three dedicated components. More precisely, an oriented proposal generation mechanism is proposed to explicitly generate oriented proposals, which provides better positional priors for pooling features to modulate the cross-attention in the transformer decoder. An adaptive oriented proposal refinement module is introduced to extract rotation-invariant region features and eliminate the misalignment between region features and objects. And a rotationaware set matching loss is used to ensure the one-to-one matching process for direct set prediction without duplicate predictions. Our method considerably simplifies the overall pipeline and presents a new AOOD paradigm. Comprehensive experiments on several challenging datasets show that our method achieves superior performance on the AOOD task. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

Integrating a Digital Twin Concept in the Zero Emission Sea Transporter (ZEST) Project for Sustainable Maritime Transport using Stonefish Simulator

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arXiv 2024年

作者： Grimaldi, Michele Cernicchiaro, Carlo Rossides, George Ktoris, Angelos Yfantis, Elias Kyriakides, Ioannis Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University United Kingdom Marine Robotics Innovation Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine & Offshore Science Technology & Engineering Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine Technology Division Cyprus Marine and Maritime Institute Larnaca Cyprus Computational Cognition Lab Open University of Cyprus Nicosia Cyprus Department of Engineering University of Nicosia Nicosia Cyprus

In response to stringent emission reduction targets imposed by theInternational Maritime Organization (IMO) and the European Green Deal\'s Fit for55 legislation package, the maritime industry has shifted its focus towardsdecarbonization. While significant attention has been placed on vesselsexceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping,amounting to approximately 13% of global shipping transportation and 15% withinthe European Union (EU), have not received adequate consideration. Thisabstract introduces the Zero Emission Sea Transporter (ZEST) project, designedto address this issue by developing a zero-emissions multi-purpose catamaranfor short sea route © 2024, CC BY.

关键词： Emission control

Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

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arXiv 2022年

作者： Conde, Marcos V. Timofte, Radu Huang, Yibin Peng, Jingyang Chen, Chang Li, Cheng Pérez-Pellitero, Eduardo Song, Fenglong Bai, Furui Liu, Shuai Feng, Chaoyu Wang, Xiaotao Lei, Lei Zhu, Yu Li, Chenghua Jiang, Yingying Yong, A. Wang, Peisong Leng, Cong Cheng, Jian Liu, Xiaoyu Yin, Zhicun Zhang, Zhilu Li, Junyi Liu, Ming Zuo, Wangmeng Jiang, Jun Kim, Jinha Zhang, Yue Zou, Beiji Zong, Zhikai Liu, Xiaoxiao Vega, Juan Marín Sloth, Michael Schneider-Kamp, Peter Röttger, Richard Kınlı, Furkan Özcan, Barış Kıraç, Furkan Leyi, Li Nadim Uddin, S.M. Ghosh, Dipon Kumar Jung, Yong Ju Computer Vision Lab CAIDAS University of Würzburg Germany Huawei Noah's Ark Lab China Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China MAICRO AiRiA Harbin Institute of Technology China SenseBrain Technology United States School of Computer Science and Engineering Central South University Hunan Engineering Research Center of Machine Vision and Intelligent Medicine China China University of Southern Denmark Esoft Systems Denmark Özyeğin University Turkey Zhejiang University China Lab School of Computing Gachon University Korea Republic of

Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public RGB datasets. This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation. The proposed methods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution. © 2022, CC BY-SA.

关键词： Inverse problems

ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans

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arXiv 2022年

作者： Chai, Zhizhong Lin, Huangjing Luo, Luyang Heng, Pheng-Ann Chen, Hao Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Most of the existing object detection works are based on the bounding box annotation: each object has a precise annotated box. However, for rib fractures, the bounding box annotation is very labor-intensive and time-consuming because radiologists need to investigate and annotate the rib fractures on a slice-by-slice basis. Although a few studies have proposed weakly-supervised methods or semi-supervised methods, they could not handle different forms of supervision simultaneously. In this paper, we proposed a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data to further improve the detection performance. Specifically, the proposed network contains an omni-supervised detection head, in which each form of annotation data corresponds to a unique classification branch. Furthermore, we proposed a dynamic label assignment strategy for different annotated forms of data to facilitate better learning for each branch. Moreover, we also design a confidence-aware classification loss to emphasize the samples with high confidence and further improve the model’s performance. Extensive experiments conducted on the testing dataset show our proposed method outperforms other state-of-the-art approaches consistently, demonstrating the efficacy of deep omni-supervised learning on improving rib fracture detection performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for computer-Aided COVID-19 Diagnostics

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arXiv 2022年

作者： Pavlova, Maya Tuinstra, Tia Aboutalebi, Hossein Zhao, Andy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Cheriton School of Computer Science University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

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arXiv 2023年

作者： Karnam, Naveen Kumar Turlapaty, Anish Chand Dubey, Shiv Ram Gokaraju, Balakrishna The Biosignal Analysis Lab The Indian Institute of Information Technology A.P. Sri City India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology U.P. Allahabad Prayagraj211015 India Department of Computational Data Science An Engineering North Carolina A and T State University GreensboroNC United States

In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-of-the-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Local binary pattern

Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds

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arXiv 2021年

作者： Dai, Dengxin Vasudevan, Arun Balajee Matas, Jiri Van Gool, Luc The Vision for Autonomous Systems Group MPI for Informatics The Computer Vision Lab ETH Zürich Switzerland The Center for Machine Perception Czech Technical University Czech Republic The Department of Electrical Engineering KU Leuven Belgium

Humans can robustly recognize and localize objects by using visual and/or auditory cues. While machines are able to do the same with visual data already, less work has been done with sounds. This work develops an approach for scene understanding purely based on binaural sounds. The considered tasks include predicting the semantic masks of sound-making objects, the motion of sound-making objects, and the depth map of the scene. To this aim, we propose a novel sensor setup and record a new audio-visual dataset of street scenes with eight professional binaural microphones and a 360°camera. The co-existence of visual and audio cues is leveraged for supervision transfer. In particular, we employ a cross-modal distillation framework that consists of multiple vision 'teacher' methods and a sound 'student' method-the student method is trained to generate the same results as the teacher methods do. This way, the auditory system can be trained without using human annotations. To further boost the performance, we propose another novel auxiliary task, coined Spatial Sound Super-Resolution, to increase the directional resolution of sounds. We then formulate the four tasks into one end-to-end trainable multi-tasking network aiming to boost the overall performance. Experimental results show that 1) our method achieves good results for all four tasks, 2) the four tasks are mutually beneficial-training them together achieves the best performance, 3) the number and orientation of microphones are both important, and 4) features learned from the standard spectrogram and features obtained by the classic signal processing pipeline are complementary for auditory perception tasks. The data and code are released on the project page: https://***/publications/2020/sound perception/***. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

Facial-Sketch Synthesis: A New Challenge

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arXiv 2021年

作者： Fan, Deng-Ping Huang, Ziling Zheng, Peng Liu, Hong Qin, Xuebin Van Gool, Luc Computer Vision Lab ETH Zürich Zürich Switzerland Information and Communication Engineering University of Tokyo Tokyo Japan Computer Vision MBZUAI Abu Dhabi United Arab Emirates Digital Content and Media Sciences Research Division NII Tokyo Japan

This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high costs of obtaining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS investigation by reviewing 89 classic methods, including 25 handcrafted feature-based facial-sketch synthesis approaches, 29 general translation methods, and 35 image-to-sketch approaches. In addition, we elaborate comprehensive experiments on the existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facial-aware masking and style-vector expansion, our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin. Finally, we conclude with lessons learned over the past years and point out several unsolved challenges. Our code is available at https://***/DengPingFan/FSGAN. © 2021, CC BY.

关键词： Large dataset