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检索条件"任意字段=Conference on Optomechatronic Computer-Vision Systems II"

共 980 条记录，以下是21-30 订阅

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VIPER: vision-Based System to Detect Potential Predators for Herding with Robots 19th

VIPER: Vision-Based System to Detect Potential Predators for...

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19th International conference on Hybrid Artificial Intelligence systems

作者： Yang, Xiao Carnicero, Abel Sanchez-Gonzalez, Lidia Rodriguez-Lera, Francisco J. Univ Leon Dept Ingn Mecan Informat & Aerosp Leon 24071 Spain

ISBN: (纸本)9783031741852;9783031741869

This paper presents a vision system that can be deployed in a ROS2 node that identifies potential predators such as wolves in order to herd autonomously a flock. A pre-trained YOLOv8 model and a pretrained YOLOv9 model have been fine tunned so as to include more classes that are not supported such as wolves, and the whole models are re-trained with the COCO dataset and the new one, keeping the recognition of the previous learned classes as well as the new ones. To do that a dataset of images of wolves in open spaces has been acquired and labelled for instance segmentation purposes. The obtained vision model is wrapped in a ROS2 node, making it possible to implement it in a robotic device either a 4-legged robot or a rover robot. With this vision-based model, a robot can determine if a potential predator is detected and provide useful information to the herder to avoid area where the flock is in danger. Extensive livestock farming can benefit from the use of this technology to facilitate the tasks required on a daily basis.

关键词： Precision Livestock Farming computer vision Herding with robots Predator detection

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Retrofitting a Legacy Cutlery Washing Machine Using computer vision 16th

Retrofitting a Legacy Cutlery Washing Machine Using Computer...

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16th International conference on Computational Collective Intelligence (ICCCI)

作者： Fwa, Hua Leong Singapore Management Univ 81 Victoria St Singapore 188065 Singapore

ISBN: (纸本)9783031702587;9783031702594

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed and utilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional image processing to be generalizable across 'unseen' images. Our proposed final solution uses traditional image processing for computation of utilization but a hybrid of traditional image processing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.

关键词： Industry 4.0 computer vision Deep Learning Image Processing

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Investigation of an Integrated Synthetic Dataset Generation Workflow for computer vision Applications 20th

Investigation of an Integrated Synthetic Dataset Generation ...

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20th International conference on Product Lifecycle Management: Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation (PLM)

作者： Rolf, Julian Wolf, Mario Gerhard, Detlef Ruhr Univ Bochum Bochum Germany

ISBN: (纸本)9783031625848;9783031625824;9783031625817

Object detection and other machine learning technology applications play an important role in various areas of computer vision (CV) applications within the product lifecycle, especially in quality assurance or general assembly assistance. While the implemented CV-based systems provide great benefits, training and implementing deep learning models is often a tedious and time-consuming task, especially in the field of object detection. To accomplish good results, large datasets with a high quantity of object instances in a bright variety of poses are required These are generally created manually and are therefore very time consuming to create. To improve the training process, synthetic training data can be used. It is generated within a virtual environment using a product's geometry model. In this paper, the authors propose a synthetic dataset generator for object detection, that is integrated into a PLM system to automate the process of collecting and processing the CAD data for creating the synthetic machine learning training dataset. Domain randomization is used to eliminate the effort of creating a virtual environment, to fully automate the dataset generation, and to increase the generalization of the model. The trained detector is tested on an object detection demonstrator set-up to evaluate its performance in a real-world use case. For evaluation purposes, the authors also provide a comparison of the test results to an object detection model that is trained without domain randomization, using a very close-to-reality virtual environment.

关键词： deep learning synthetic data generation PLM integration object detection computer vision

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SALAD : Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection 23

SALAD : Source-free Active Label-Agnostic Domain Adaptation ...

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23rd IEEE/CVF Winter conference on Applications of computer vision (WACV)

作者： Kothandaraman, Divya Shekhar, Sumit Sancheti, Abhilasha Ghuhan, Manoj Shukla, Tripti Manocha, Dinesh Univ Maryland College Pk College Pk MD 20742 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665493468

We present a novel method, SALAD, for the challenging vision task of adapting a pre-trained "source" domain network to a "target" domain, with a small budget for annotation in the "target" domain and a shift in the label space. Further, the task assumes that the source data is not available for adaptation, due to privacy concerns or otherwise. We postulate that such systems need to jointly optimize the dual task of (i) selecting fixed number of samples from the target domain for annotation and (ii) transfer of knowledge from the pre-trained network to the target domain. To do this, SALAD consists of a novel Guided Attention Transfer Network (GATN) and an active learning function, H-AL. The GATN enables feature distillation from pre-trained network to the target network, complemented with the target samples mined by H-AL using transfer-ability and uncertainty criteria. SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection;(ii) it can handle shifts in output label space from the pre-trained source network to the target domain;(iii) it does not require access to source data for adaptation. We conduct extensive experiments across 3 visual tasks, viz. digits classification (MNIST, SVHN, VISDA), synthetic (GTA5) to real (CityScapes) image segmentation, and document layout detection (PubLayNet to DSSE). We show that our source-free approach, SALAD, results in an improvement of 0.5%-31.3% (across datasets and tasks) over prior adaptation methods that assume access to large amounts of annotated source data for adaptation. Code is available here.

关键词： Knowledge engineering Visualization Image segmentation Data privacy computer vision Uncertainty Codes

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Improved YOLOv5 with Backbone Replacement to MobileNetv3 for Weapons Detection: Application for Smart Video Surveillance 4th

Improved YOLOv5 with Backbone Replacement to MobileNetv3 for...

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4th International conference on Digital Technologies and Applications (ICDTA)

作者： Khalfaoui, Aicha Abdelmajid, Badri Ilham, El Mourabit Hassan II Univ Casablanca Fac Sci & Tech Mohammedia LEEA & TI Lab Casablanca Morocco

ISBN: (纸本)9783031686528;9783031686535

In the field of computer vision technology recent advancements have made improvements, in object detection impacting domains such as transportation and security surveillance. One important use case involves the real time identification of weapons for law enforcement and monitoring purposes. This study focuses on comparing the performance of two object detection models;YOLOv5s and MobileNetv3 YOLOv5s, based on factors like accuracy, computational efficiency and parameter count. Our models were trained using the Sohas Weapon Dataset and Gun Movies Dataset to address orientations and lighting conditions. Themain objectivewas to optimize the models for efficiency while maintaining an Average Precision (mAP). The results indicate a reduction in parameters, weight and GFLOPs;however, there was also a decrease, in mAP and recall metrics.

关键词： computer vision Yolov5 MobileNetV3s weapons detection

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Autonomous AI-enabled Industrial Sorting Pipeline for Advanced Textile Recycling 20

Autonomous AI-enabled Industrial Sorting Pipeline for Advanc...

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20th Annual International conference on Distributed Computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Spyridis, Yannis Argyriou, Vasileios Sarigiannidis, Antonios Radoglou, Panagiotis Sarigiannidis, Panagiotis Kingston Univ Dept Networks & Digital Media London England K3Y Ltd Sofia Bulgaria Univ Western Macedonia Dept Informat & Telecommun Engn Kozani Greece

ISBN: (纸本)9798350369458;9798350369441

The escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion industry. This paper addresses the inefficiencies of traditional textile sorting methods by introducing an autonomous textile analysis pipeline. Utilising robotics, spectral imaging, and AI-driven classification, our system enhances the accuracy, efficiency, and scalability of textile sorting processes, contributing to a more sustainable and circular approach to waste management. The integration of a Digital Twin system further allows critical evaluation of technical and economic feasibility, providing valuable insights into the sorting system's accuracy and reliability. The proposed framework, inspired by Industry 4.0 principles, comprises five interconnected layers facilitating seamless data exchange and coordination within the system. Preliminary results highlight the potential of our holistic approach to mitigate environmental impact and foster a positive shift towards recycling in the textile industry.

关键词： Textile recycling Autonomous systems computer vision Artificial intelligence Industry 4.0

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RANCER: Non-Axis Aligned Anisotropic Certification with Randomized Smoothing 23

RANCER: Non-Axis Aligned Anisotropic Certification with Rand...

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23rd IEEE/CVF Winter conference on Applications of computer vision (WACV)

作者： Rumezhak, Taras Eiras, Francisco Girbal Torr, Philip H. S. Bibi, Adel Ukrainian Catholic Univ SoftServe Lvov Ukraine Univ Oxford Oxford England

ISBN: (纸本)9781665493468

As modern networks have been proven to be unprotected from adversarial attacks and are applied in safety-critical applications, defense against them is very crucial. Many works were dedicated to this topic, but randomized smoothing has been recently proven to be an effective approach for the certified defense of deep neural networks and getting robust classifiers. Some prior results were obtained utilizing the techniques of adding extra parameters to extend the limits of the certification regions. In this way, sample-wise optimization was proposed to maximize the certification radius per input. The idea was further extended with the generalized anisotropic counterparts of l(1) and l(2) certificates which allow achieving larger certified region volume avoiding worst-case certification near potentially larger safe regions. However, anisotropic certification is limited by the aligned axis lacking the freedom to extend in any direction. To mitigate this constraint, in this work, we (i) revisit the anisotropic certification, provide an analysis of its non-axis aligned counterpart and propose its rotation-free extension, (ii) conduct experiments on the CIFAR-10 dataset to report the improved performance.

关键词： Deep learning computer vision Smoothing methods Neural networks Certification Optimization

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Innovative Deep Learning Techniques for Solving Complex computer vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International conference on Distributed systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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computer vision and Its Algorithmic Evolution in Image Understanding 7

Computer Vision and Its Algorithmic Evolution in Image Under...

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7th International conference on Contemporary Computing and Informatics, IC3I 2024

作者： Smitha, B.S. Jain, Alok Sreenivasulu, D. Sharma, Saurabh Reddy, B. Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Automobile Engineering Bangalore India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

As computer vision systems have improved, mostly through machine vision progress, people's views on pictures have changed a lot. In response to this change, the Dynamic AdaptNet project is making an advanced system for analysing pictures that uses data from the surroundings. The manuscripts goals is to offer a full range of features, such as dynamic convolutional neural networks (CNNs), adaptable segmentation, and temporal attention. Standard, Baseline, Enhanced, Advanced, Optimised, and Specialised are some of the choices for the system. Standard is the easiest and most important to use. This study compares and contrasts different methods by checking how well they work based on several factors, such as success, adaptability, and resilience. Independent reviews and comparisons show that Dynamic AdaptNet is a complete tool for real-time picture analysis. This shows that it has the ability to make computer vision better. © 2024 IEEE.

关键词： Convolutional neural networks

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OffloaDNN: Shaping DNNs for Scalable Offloading of computer vision Tasks at the Edge 44

OffloaDNN: Shaping DNNs for Scalable Offloading of Computer ...

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44th IEEE International conference on Distributed Computing systems (ICDCS)

作者： Puligheddu, Corrado Varshney, Nancy Hassan, Tanzil Ashdown, Jonathan Restucciat, Francesco Chiasserinit, Carla Fabiana Politecn Torino Turin Italy Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Air Force Res Lab Wright Patterson AFB OH USA Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798350386066;9798350386059

Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute- and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease resource consumption at the mobile devices, it poses the challenge of handling multiple concurrent tasks with limited computing and memory capacity. In stark opposition with the existing state of the art, we tackle this challenge by jointly optimizing (i) the utilization of resources at the edge, among which memory - so far widely overlooked - and the radio resources used for task offloading;(ii) which and how many offloaded tasks should be executed;and (iii) the structure of the DNNs. First, we formulate the DNN for scalable Offloading of Tasks (DOT) problem, prove that it is NP-hard, and envision a weighted-tree-based heuristic solution, named OffloaDNN, that efficiently solves the DOT problem. We evaluate OffloaDNN through extensive numerical analysis using state-of-the-art image classification ResNet-18, as well as real-world experiments on the Colosseum emulator. The numerical results show that, in small-scale scenarios, OffloaDNN matches the optimum very closely, and, in larger-scale scenarios, increases the number of admitted offloaded tasks by 26.9% with respect to the state of the art, while saving 82.5% memory and 77.4% per-inference computing time. The numerical results are confirmed by the real-world validation on Colosseum.

关键词： Image classification

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