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An efficient strawberry segmentation model based on Mask R-CNN and TensorRT

Artificial Intelligence in Agriculture 2025年第2期15卷 327-337页

作者： Anthony Crespo Claudia Moncada Fabricio Crespo Manuel Eugenio Morocho-Cayamcela Yachay Tech University School of Mathematical and Computational Sciences Urcuqui 100115ImbaburaEcuador Deep Learning for Autonomous Vehicles Roboticsand Computer Vision(DeepARC Research)Urcuqui 100115ImbaburaEcuador

Currently,artificial intelligence(AI),particularly computer vision(CV),has numerous applications in *** this field,the production and consumption of strawberries have experienced great growth in recent years,which makes meeting the growing demand a challenge that producers must ***,one of the main problems regarding the cultivation of this fruit is the high cost and long picking *** response,automatic harvesting has surged as an option to address this difficulty,and fruit instance segmentation plays a crucial role in these types of *** segmentation is related to the identification and separation of individual fruits within a crop,allowing a more efficient and accurate harvesting *** deep learning(DL)techniques have shown potential for this activity,the complexity of the models leads to difficulty in their implementation in real-time *** this reason,a model capable of performing adequately in real-time,while also having good precision is of great *** this motivation,this work presents a efficient Mask R-CNN model to perform instance segmentation in strawberry *** efficiency of the model is assessed considering the amount of frames per second(FPS)it can process,its size in megabytes(MB)and its mean average precision(mAP)*** approaches are provided:The first one consists on the training of the model using the Detectron2 library,while the second one focuses on the training of the model using the NVIDIA TAO *** both cases,NVIDIA TensorRT is used to optimize the *** results show that the best Mask R-CNN model,without optimization,has a performance of 83.45 mAP,4 FPS,and 351 MB of size,which,after the TensorRT optimization,achieved 83.17 mAP,25.46 FPS,and only 48.2 MB of *** represents a suitable model for implementation in real-time systems.

关键词： Deep learning Image segmentation Mask R-CNN Strawberry segmentation computer vision

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Eye Direction Detection as Wheelchair Navigation Using Faster R-Cnn 23

Eye Direction Detection as Wheelchair Navigation Using Faste...

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8th International Conference on Sustainable Information Engineering and Technology, SIET 2023

作者： Irsal, Riyandi Banovbi Putera Laksono, Blessius Sheldo Putra Utaminingrum, Fitri Computer Vision Research Group Universitas Brawijaya Indonesia Robotics and Embedded System Research Group Universitas Brawijaya East Java Indonesia

ISBN: (纸本)9798400708503

In this life, many people with disabilities lose the opportunity to live better than others. especially people with physical disabilities who may not have mobility like us. Physical disability is caused by several things, some from birth or disease and also an accident. People with physical disabilities who have difficulty walking require a wheelchair to be able to move. However, hands and arms must be used to operate the wheelchair. We found a way to solve the problem. we plan to move the wheelchair with eyeball movement as navigation. this solution does not only solve the problem of physical disability. we can also reduce contact to often hold the wheelchair. To detect eyeball movement, we use the Faster R-CNN method. This method if we compare it with other methods has competitive accuracy. From the results we have, we get a mAP value of more than 0.853 from each class. © 2023 ACM.

关键词： Wheelchairs

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Towards Source-Free Domain Adaptive Semantic Segmentation Via Importance-Aware and Prototype-Contrast Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha China College of Engineering and Design Hunan Normal University Changsha China

Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. IEEE

关键词： Semantics

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A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

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SN computer Science 2024年第6期5卷 689页

作者： Ramos, Leo Pineda, Israel School of Mathematical and Computational Sciences Yachay Tech University Deep Learning for Autonomous Driving Robotics and Computer Vision (DeepARC) Research Group Yachay Tech University Kauel Inc. Computer Vision Center Universitat Autònoma de Barcelona College of Sciences and Engineering Universidad San Francisco de Quito

Precise biomedical image segmentation is pivotal in medical diagnosis and treatment. Among various methodologies, image processing-based techniques are useful due to their swift and consistent results, free from the dependency on extensive training data required by deep learning approaches. This study introduces a tri-phase, semi-automated pipeline for lung computed tomography image segmentation, beginning with a preprocessing phase that includes contrast enhancement, noise reduction, and edge detection to improve image quality. This is followed by the application of the region growing method for segmentation and is completed with morphological closing to refine the final output. Comparative tests reveal that our technique surpasses prevalent methods like split and merge, Otsu’s technique, and basic region growing in terms of the dice coefficient (DC) and intersection over union (IoU). Specifically, our approach achieved an average DC of 0.9633, contrasting with 0.4668, 0.005, and 0.4067 recorded by Otsu’s, split and merge, and basic region growing techniques respectively. Similarly, the average IoU for our method was 0.9341, in comparison to 0.3105, 0.0660, and 0.2605 for the aforementioned methods. Additionally, comparative analysis with contemporary segmentation models demonstrates that our approach is competitive, even without relying on large training datasets. These results highlight our method’s efficiency and practicality, distinguishing it as a compelling option in medical scenarios where access to extensive training data is limited. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis Image processing Image segmentation Medical image Pattern recognition Region growing

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power Systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Exploiting autoencoder’s weakness to generate pseudo anomalies

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Neural Computing and Applications 2024年第23期36卷 14075-14091页

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data are often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Underwater Acoustic Localization using pose-graph SLAM

Underwater Acoustic Localization using pose-graph SLAM

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OCEANS

作者： Marta Real Pau Vial Narcís Palomeras Marc Carreras Computer Vision and Robotics Research Institute (VICOROB) University of Girona Girona Spain

Current acoustic localization systems for Autonomous Underwater Vehicles (AUV) rely on acoustic beacons at known positions. We present a method that combines rangeonly acoustic measures and odometry data into a factor graph structure, able to compute the AUV localization while estimating the position of the acoustic beacons without having previous information about their position. We designed several missions and carried them out in a real environment to gather data to test our algorithm offline. The output of this work is a pose-graph SLAM algorithm that combines the AUV internal measurements (i.e. DVL, IMU, and depth) with acoustic ranges from static beacons to estimate both the AUV state and the beacon’s position.

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Inertial navigation framework for multimodal underwater Graph SLAM

Inertial navigation framework for multimodal underwater Grap...

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OCEANS

作者： Pau Vial Miguel Castillón Narcís Palomeras Marc Carreras Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) University of Girona Girona Spain

Robot localization is a fundamental task in achieving true autonomy for Autonomous Underwater Vehicles (AUV). If inertial measurements from an Inertial Measurement Unit (IMU) or a Doppler Velocity Log (DVL) want to be fused with some perception system, such us a multibeam sonar or several acoustic beacons; a full Simultaneous Localization And Mapping (SLAM) problem must be solved. In contrast to filters, in a full SLAM problem the whole robot trajectory is estimated and loop closure events can be detected and closed along it. Common Inertial Navigation Systems (INS), based on filters, only maintain the estimation of the current robot pose. Therefore, these systems cannot be directly used in a full SLAM problem. In this paper we present a graph solution to integrate all inertial measurements in a factor graph that can be extended to different perception modalities and it is solved by applying Smoothing and Mapping (SAM) [1]. The Preintegrated IMU factor, proposed by [2], is combined with priors for other inertial measurements that have been specially designed. This framework is tested on real data from sea experiments, showing how our proposal performance is similar to the estimation provided by high grade commercial INS products based on filters. However, our system has the advantage of allowing for fusion with exteroceptive sensors in SLAM.

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A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation 37

A Single 2D Pose with Context is Worth Hundreds for 3D Human...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhao, Qitao Zheng, Ce Liu, Mengyuan Chen, Chen Robotics Institute Carnegie Mellon University United States Center for Research in Computer Vision University of Central Florida United States Key Laboratory of Machine Perception Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713899921

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [74], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer. © 2023 Neural information processing systems foundation. All rights reserved.

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GMM Registration: a Probabilistic scan matching approach for sonar-based AUV navigation

GMM Registration: a Probabilistic scan matching approach for...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Pau Vial Miguel Malagón Ricard Segura Narcís Palomeras Marc Carreras Computer Vision and Robotics Research Institute (VICOROB) Universitat de Girona Girona Catalonia

Acoustic perception in underwater environments is challenging due to the low frequency of the acquisition system and multiple and huge sources of noise. Therefore, point clouds built by profiling sonars mounted on Autonomous Underwater Vehicles (AUV) are sparse and noisy. To solve the mapping task, AUVs need a registration algorithm to prevent maps from inconsistencies. Many scan matching algorithms are available, however, a few of them are specialized in acoustic data. In this paper, a probabilistic scan matching methodology based on Gaussian Mixtures Models (GMM) is presented and, for the first time, the Bayesian-GMM algorithm is applied in this context to model acoustic data. The scan matching problem is properly formulated using Lie groups to define pose. In addition, this methodology can return an uncertainty measure for the matching result, which is fundamental in Pose SLAM applications. This tool is implemented in a public C++library 1 1 The library repository can be found in https://***/gmmregistration/gmm_registration. that can process in real-time 2D and 3D scans acquired by a profiling sonar. Theoretical justification and results with real data are provided to benchmark our method against the state-of-the-art Normal Distributions Transforms (NDT) technique.

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