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检索条件"任意字段=6th International Conference on Computer Vision and Image Processing, CVIP 2021"

共 1235 条记录，以下是11-20 订阅

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management 6

Waste Sorting System Using AI: Development of An Embedded So...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Remili, Amal Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. this paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. the data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. the system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge. © 2025 IEEE.

关键词： AI models computer vision Waste detection Waste management Waste sorting

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach 6

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Alsubaie, Norah A. Almalki, Ghayda A. Almutairi, Ghada N. Alrumaih, Sarah A. Princess Nourah Bint Abdulrahman University Department of Computer Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798331506520

this research introduces "Jaddah,"an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. the system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. the YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. these results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency. © 2025 IEEE.

关键词： image enhancement

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A computer vision Approach for Autonomous Cars to Drive Safe at Construction Zone 6

A Computer Vision Approach for Autonomous Cars to Drive Safe...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Ahammed, Abu Shad Shahi Amran Hossain, Md Obermaisser, Roman University of Siegen Chair of Embedded Systems Siegen Germany

ISBN: (纸本)9798331506520

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. the autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. this paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. the model developed with the YOLO framework achieved a mean average precision exceeding 94% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles. © 2025 IEEE.

关键词： Autonomous vehicles

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control 6

Authentication and Verification in Human-Robot Cooperative R...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

the integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

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Event-based Sensor Fusion and Application on Odometry: A Survey 6

Event-based Sensor Fusion and Application on Odometry: A Sur...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Zhang, Jiaqiang Yu, Xianjia Sier, Ha Zhang, Haizhou Westerlund, Tomi University of Turku TIERS Lab Turku Finland

ISBN: (纸本)9798331506520

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness. they offer notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. these distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this survey presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly investigating fusion strategies that incorporate frame-based cameras, inertial measurement units, and LiDAR. the survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications. © 2025 IEEE.

关键词： computer vision

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Development of a multi-modality based approach for Plastic Waste Segregation 6

Development of a multi-modality based approach for Plastic W...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Smari, Wejdene Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Plastic waste is becoming nowadays a huge environmental problem. Circular economy seems to be the best choice for preserving the environment, by reinserting plastic waste into the industrial process and use. A key step for that is the plastic waste segregation from other waste. this paper proposes a new plastic waste segregation approach based on two modalities: spectroscopy and optical images. the research handles an optical camera for waste image acquisition, while the ground truth for plastic type identification, with respect to the seven primary plastic types: PET, HDPE, LDPE, PP, PS, PVC, and Other, is established through spectroscopic analysis. Various deep learning models were trained and evaluated on a dataset of publicly available and custom-collected images, aiming to achieve human-level accuracy in distinguishing between plastic types. the proposed methodology is based on a set of steps, where the model is first built implicitly on a segregation of different wastes, and then applied via transfer learning for separating the seven plastic types. the system performances demonstrate the feasibility and effectiveness of this combined approach for accurate and efficient plastic waste sorting. the proposed system has been implemented on Raspberry Pi 4 Model B+ as the central processing unit, integrating computer vision and deep learning techniques for efficient and accurate sorting. © 2025 IEEE.

关键词： computer vision deep learning embedded system Plastic waste segregation Raspberry Pi YOLOv8

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computer-Generated image Forensics Based on vision Transformer with Forensic Feature Pre-processing Module 6th

Computer-Generated Image Forensics Based on Vision Transfor...

引用

6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Chen, Yifang Wen, Guanchen Wang, Yong Yang, Jianhua Zhang, Yu Guangdong Polytechnic Normal University GuangDong GuangZhou510665 China

ISBN: (纸本)9783031736988

the correct distinction between highly realistic computer-generated (CG) images and photographic (PG) images has become an important area of research. In recent years, most of the CG image forensics methods are proposed based on deep learning, but the detection performances of these methods still need to be improved, especially in terms of robustness and generalization. To tackle these issues, we leverage the vision Transformer (ViT) model, which excels in capturing the global features of images, and design a Forensic Feature Pre-processing (FFP) module to further improve the detection performance. Experiments are conducted on a large-scale CG image benchmark (LSCGB), which is a challenging dataset for CG image detection. the proposed approach can achieve high detection accuracy. Extensive experiments on different public datasets and common post-processing operations demonstrate our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： computer forensics

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Occlusion-aware Data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments 6

Occlusion-aware Data Augmentation and Reconstruction for Imp...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Pham, Vincent Singh, Sonit University of New South Wales School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798331506520

the demand for adaptive learning in education is an important application for computer vision (CV)-based detection models which extract learners' faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners' faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection. © 2025 IEEE.

关键词： Contrastive Learning

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Segmentation of Ship Propeller Cavitation Area Using image-Based Anomaly Detection Network 6

Segmentation of Ship Propeller Cavitation Area Using Image-B...

引用

6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Jeong, Wonje Shin, Yong-Jin Park, Soon-Yong Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Samsung Heavy Industries Co. Ltd. Daejeon34051 Korea Republic of

ISBN: (纸本)9798331506520

Cavitation is the formation of vapor-filled cavities in the liquid. Cavitation in rotating propeller can cause various engineering problems to ships, such as hull vibration, noise, thrust reduction, and propeller corrosion. To address this cavitation problem in designing ship propellers, it needs to analyze the cavitation phenomenon in real propellers. In this study, we introduce a computer vision method to detect and segment cavitation areas in a rotating propeller. the pro-posed method captures the images of a high-speed rotating propeller in a water tunnel and detect the image area of the cavitation. As the first study in the im-age-based automatic cavitation detection, we employ a deep neural network, an anomaly detection method called DDAD (Anomaly Detection with Conditioned Denoising Diffusion Models). In addition, we also use a STN (Spatial Transformer Networks) network to compensate the image shaking caused by the vibration from the high-speed propeller and water flow. After the STN compensation, normal and abnormal images are automatically aligned to find cavitation areas more accurately. the proposed method is the first to use an anomaly detection network for ship propeller cavitation detection. Experimental results show that the proposed method can automatically detect the cavitation area without any annotation supervision. © 2025 IEEE.

关键词： Cavitation

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Botanical Bio vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. this study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. this approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. this research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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