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Advanced machine vision techniques for real-time quality detection and grading of navel oranges using high-resolution imaging and deep learning algorithms

Advanced machine vision techniques for real-time quality det...

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International Conference on Computer vision, robotics, and Automation Engineering, CRAE 2024

作者： Zhang, xiaoyan Beijing Institute of Technology Zhuhai Zhuhai519000 China

ISBN: (纸本)9781510682283

In recent years, the rapid development of computer vision and artificial intelligence has significantly advanced agricultural applications, particularly in the quality detection and grading of navel oranges. This review explores the latest advancements in machine vision technologies for navel orange quality assessment, focusing on image processing, deep learning algorithms, and multispectral imaging. Image processing techniques, fundamental to machine vision systems, enable the extraction of visual features such as color, shape, and texture, enhancing detection accuracy. Recent studies have demonstrated the efficacy of deep learning algorithms, particularly convolutional neural networks (CNNs), in achieving high-precision grading and real-time performance. Furthermore, multispectral and hyperspectral imaging technologies offer rich spectral information, facilitating more accurate quality detection and maturity assessment. Despite significant progress, challenges such as complex natural environments, lighting conditions, and the high cost of imaging equipment persist. Future research directions include integrating multi-source data fusion, developing efficient deep learning algorithms, and promoting cost-effective imaging technologies. Our proposed advanced algorithm employs the DAHENG machine vision experimental platform and MER-132-30UC camera for high-resolution image acquisition, with HALCON software for sophisticated image processing and real-time classification. This system enhances accuracy and efficiency in navel orange quality detection and grading, addressing key challenges in current agricultural practices. © 2024 SPIE.

关键词： Citrus fruits

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Mathematical modeling and problem solving: from fundamentals to applications

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JOURNAL OF SUPERCOMPUTING 2024年第10期80卷 14116-14119页

作者： Ohue, Masahito Sasayama, Kotoyu Takata, Masami Tokyo Inst Technol Sch Comp Dept Comp Sci Yokohama Kanagawa 2268501 Japan Mitsubishi Elect Bldg Solut Corp Elevator & Escalator Dev Dept Aichi 4928682 Japan Nara Womens Univ Res Grp Informat & Commun Technol Life Nara 6308506 Japan

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas.

关键词： Mathematical modeling machine learning Parallel and distributed computing Drug discovery and materials science

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Detection of Partially Occluded Area in Images Using Image Segmentation Technique 1

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4th Congress on Intelligent Systems (CIS)

作者： Cherapanamjeri, Jyothsna Rao, B. Narendra Kumar Jawaharlal Technol Univ Anantapur Andhra Pradesh India Sri Vidyanikethan Engn Coll Tirupati India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

Computer vision is subfield of artificial intelligence. Detection of occluded area in images is one of the challenging tasks in the computer vision. In this paper, we concentrate on occluded area detection in images using image segmentation techniques. Occlusion is nothing but one object in image is hidden by another object. Detection of occluded area in images using image segmentation techniques. Image segmentation is the process of dividing the images into different regions based on the characteristics of the pixels in the original image and reduces the complexity of analysis. When determining the number of objects in a scene, instance segmentation is an excellent choice. But in semantic segmentation algorithms if all that is required is to group items that belong to the same class. Image segmentation has many applications in a number of fields, including gaming, robotics, autonomous vehicles, agriculture, object detection, and pedestrian detection. In this research work, we concentrate on detection of occluded area in images using Mask R-CNN. The experiment will show better segmentation results corresponding to the manually labeled occluded area.

关键词： artificial intelligence machine learning Deep learning Computer vision Image segmentation Occluded images

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A machine vision Perspective on Droplet-Based Microfluidics

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ADVANCED SCIENCE 2025年第8期12卷 e2413146页

作者： Wang, Ji-xiang Wang, Hongmei Lai, Huang Liu, Frank x. Cui, Binbin Yu, Wei Mao, Yufeng Yang, Mo Yao, Shuhuai Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Hebei Vocat Univ Technol & Engn Hebei Key Lab Man Machine Environm Thermal Control Handan 054000 Hebei Peoples R China Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Kowloon Clear Water Bay Hong Kong 999077 Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Biomed Engn Kowloon Hong Kong 999077 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Kowloon Clear Water Bay Hong Kong 999077 Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225009 Peoples R China Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China

Microfluidic droplets, with their unique properties and broad applications, are essential in in chemical, biological, and materials synthesis research. Despite the flourishing studies on artificial intelligence-accelerated microfluidics, most research efforts have focused on the upstream design phase of microfluidic systems. Generating user-desired microfluidic droplets still remains laborious, inefficient, and time-consuming. To address the long-standing challenges associated with the accurate and efficient identification, sorting, and analysis of the morphology and generation rate of single and double emulsion droplets, a novel machine vision approach utilizing the deformable detection transformer (DETR) algorithm is proposed. This method enables rapid and precise detection (detection relative error < 4% and precision > 94%) across various scales and scenarios, including real-world and simulated environments. Microfluidic droplets identification and analysis (MDIA), a web-based tool powered by Deformable DETR, which supports transfer learning to enhance accuracy in specific user scenarios is developed. MDIA characterizes droplets by diameter, number, frequency, and other parameters. As more training data are added by other users, MDIA's capability and universality expand, contributing to a comprehensive database for droplet microfluidics. The work highlights the potential of artificial intelligence in advancing microfluidic droplet regulation, fabrication, label-free sorting, and analysis, accelerating biochemical sciences and materials synthesis engineering.

关键词： artificial intelligence data-driven automation intelligent multiphase flows label-free method microfluidic droplets

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Sensors, systems and algorithms of 3D reconstruction for smart agriculture and precision farming: A review

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Yu, Shuwan Liu, xiaoang Tan, Qianqiu Wang, Zitong Zhang, Baohua Nanjing Agr Univ Coll Artificial Intelligence Nanjing Jiangsu Peoples R China Southeast Univ Coll Automat Nanjing Jiangsu Peoples R China

Perceiving the shape and structure of the real three-dimensional world through sensors and cameras is indispensable across various domains. The 3D reconstruction technology is dedicated to realizing this ideal process. 3D reconstruction technology serves as a transformative tool, enriching our ability to perceive the genuine shape and stereo structure of objects and scenes in the real world. Through combining advanced sensors, image processing algorithms and 3D reconstruction methods, it captures the shape and structural information of targets from multiple perspectives and dimensions, and creates highly realistic 3D models in the virtual environment. With the rapid modernization of agriculture and ongoing technological progress, the demand for more efficient and precise management and monitoring methods in agricultural production is increasing. Traditional observation and measurement methods face challenges such as low efficiency and incomplete data. 3D reconstruction technology provides more accurate and intelligent management tools for smart agriculture. This paper provides a detailed introduction to the research progress based on 3D reconstruction technology in smart agriculture. It delves into the characteristics and development of various sensors and sensing systems, discussing various methods to implement 3D reconstruction technology. Different from applications in industrial environments, agricultural environments and crops are usually complex and variable, and consideration of diverse factors is required for the selection of suitable sensors and reconstruction methods. Therefore, several aspects of applications are summarized, such as agricultural robotics, crop phenotyping, livestock, and the food industry. Finally, the challenges and potential future trends of 3D reconstruction in agriculture are given.

关键词： 3D reconstruction Smart agriculture Precision farming machine vision Agricultural robotics Crop phenotyping

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machine Learning Models for Gait Phases Detection Using Surface Electromyography Signals 17th

Machine Learning Models for Gait Phases Detection Using Surf...

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17th International Conference on Intelligent robotics and applications

作者： Feng, Zhiyang Jiang, Zhengxuan Liu, Huayue Wang, Wenkong Wang, Yiqi Lu, Chang Ma, xin Li, Yibin Song, Rui Zhang, Huanghe Shandong Univ Sch Control Sci & Engn Ctr Robot Jinan Shandong Peoples R China Univ Warwick Coventry W Midlands England Heidelberg Univ Fac Med Heidelberg Germany Heidelberg Univ Hosp Inst Computat Biomed Heidelberg Germany

ISBN: (纸本)9789819607853;9789819607860

Accurate gait phase detection is a fundamental technology for wearable rehabilitation robots, enabling them to deliver effective, safe, and personalized assistance that closely aligns with the therapeutic needs and safety requirements of users. Unlike force sensors or inertial measurement units, surface electromyography (sEMG) signals establish a direct link to the wearer's motion intentions, making them ideally suited for real-time applications. This study investigates the utilization of sEMG signals to identify gait phases using three types of machine learning models: k-nearest neighbors (KNN), support vector machines (SVM), and artificial neural networks (ANN). The models are trained on a publicly available dataset (i.e., SIAT-LLMD), which consists of data from 40 healthy individuals and utilizes three training methods: holdout, 10-fold cross-validation, and leave-one-subject-out cross-validation. ANN achieved the best results, demonstrating an overall accuracy of 92.6%+/- 2.6% for detecting stance and swing phases, and 94.4%+/- 4.2% for detecting five gait sub-phases (i.e., stance flexion phase, stance extension phase, preswing phase, swing flexion phase, and swing extension phase).

关键词： EMG signals Gait phase detection machine learning artificial neural networks

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machine Learning applications: From Computer vision to robotics

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2024年

作者： Chatterjee Indranath

ISBN: (数字)9781394173341;9781394173334

ISBN: (纸本)9781394173327

machine Learning applications Practical resource on the importance of machine Learning and Deep Learning applications in various technologies and real-world situations machine Learning applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, machine Learning applications includes information on:

Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records

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Special Issue: machine Learning and Representation Issues in CAD/CAM

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第1期24卷 010301-010301页

作者： Purwar, Anurag Desai, Kaushalkumar A. Canfield, Stephen Rai, Rahul Nie, Zhenguo SUNY Stony Brook Dept Mech Engn Comp Aided Design & Innovat Lab Stony Brook NY 11794 USA Indian Inst Technol Jodhpur Dept Mech Engn Jodhpur 342030 Rajasthan India Tennessee Technol Univ Dept Mech Engn Cookeville TN 38505 USA Clemson Univ Dept Mech Engn Clemson SC 29634 USA Tsinghua Univ Dept Mech Engn Beijing 100190 Peoples R China

machine learning (ML), a sub-field of artificial intelligence (AI), is profoundly reshaping various aspects of human life. Its application in engineering systems promises to address long-standing challenges, although it also introduces new questions. While the potential of ML is undeniable, integrating existing ML methods into computer-aided design and manufacturing (CAD/CAM) presents distinctive challenges. These challenges encompass representation, adaptation, and the development of novel ML techniques to enhance CAD/CAM systems for diverse design and manufacturing solutions. This special issue aims to explore and resolve issues related to effective engineering model representation for ML, the integration of neural networks and deep generative models into CAD/CAM, mathematical frameworks combining ML with CAD/CAM in geometry and topology, data interpretation, and physics-based learning. The issue features ten papers that delve into various topics, including material prediction for assemblies, robotics mechanism design, multi-modal ML in engineering design, data-driven component segmentation in engineering drawings, evaluating assembly-part semantic knowledge in language models, three-dimensional slice reconstruction from high-resolution 2D images, physics-informed neural networks to expedite thermal simulations in additive manufacturing, transfer learning for defect detection in steel strips, AI-aided hull form design for energy-efficient unmanned underwater vehicles, and real-time high-precision calibration of quadruped robots using machine vision and artificial neural networks. Below, we provide concise summaries of each of the ten papers published in this special issue. The representation of mechanisms and their attributes for ML applications within engineering has hitherto remained unexplored. In their paper entitled “An Invariant Representation of Coupler Curves Using a Variational AutoEncoder: Application to Path Synthesis of Four-Bar Mechanisms,” Nurizada a

关键词： artificial neural networks Manufacturing Design machine learning Computer-aided design

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Brain-inspired Intelligent robotics:Theoretical Analysis and Systematic Application

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machine intelligence Research 2023年第1期20卷 1-18页

作者： Hong Qiao Ya-xiong Wu Shan-Lin Zhong Pei-Jie Yin Jia-Hao Chen The State Key Laboratory of Management and Control for Complex System Institute of AutomationChineseAcademyof SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Chinese Academy of Science Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China

Traditional joint-link robots have been widely used in production lines because of their high precision for single *** the development of the manufacturing and service industries,the requirement for the comprehensive performance of robotics is *** types of bio-inspired robotics have been investigated to realize human-like motion control and manipulation.A study route from inner mechanisms to external structures is proposed to imitate humans and animals *** this idea,a brain-inspired intelligent robotic system is constructed that contains visual cognition,decision-making,motion control,and musculoskeletal *** paper reviews cutting-edge research in brain-inspired visual cognition,decision-making,motion control,and musculoskeletal *** software systems and a corresponding hardware system are established,aiming at the verification and applications of next-generationbrain-inspired musculoskeletal robots.

关键词： Brain-inspired intelligent robot software and hardware decision making muscle control cognitive intelligence.

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VADS: Visuo-Adaptive DualStrike attack on visual question answer

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COMPUTER vision AND IMAGE UNDERSTANDING 2024年 249卷

作者： Zhang, Boyuan Li, Jiaxu Shi, Yucheng Han, Yahong Hu, Qinghua Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Tianjin Key Lab Machine Learning Tianjin Peoples R China Sch Comp & Artificial Intelligence 100 Sci Ave Zhengzhou Henan Peoples R China

Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. The adversarial vulnerability of VQA models is crucial for their reliability in real-world applications. However, current VQA attacks are mainly focused on the white-box and transfer-based settings, which require the attacker to have full or partial prior knowledge of victim VQA models. Besides that, query-based VQA attacks require a massive amount of query times, which the victim model may detect. In this paper, we propose the Visuo-Adaptive DualStrike (VADS) attack, a novel adversarial attack method combining transfer-based and query-based strategies to exploit vulnerabilities in VQA systems. Unlike current VQA attacks focusing on either approach, VADS leverages a momentum-like ensemble method to search potential attack targets and compress the perturbation. After that, our method employs a query-based strategy to dynamically adjust the weight of perturbation per surrogate model. We evaluate the effectiveness of VADS across 8 VQA models and two datasets. The results demonstrate that VADS outperforms existing adversarial techniques in both efficiency and success rate. Our code is available at: https://***/stevenzhang9577/VADS.

关键词： Cross-modal robustness Visual question answer Adversarial attack

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