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Semantic segmentation of agricultural images:A survey

Information processing in Agriculture 2024年第2期11卷 172-186页

作者： Zifei Luo Wenzhu Yang Yunfeng Yuan Ruru Gou Xiaonan Li School of Cyber Security and Computer Hebei UniversityHebei 071002China Hebei Machine Vision Engineering Research Center BaodingHebei 071002China College of Education Hebei UniversityHebei 071002China

As an important research topic in recent years,semantic segmentation has been widely applied to image understanding problems in various *** the successful application of deep learning methods in machine vision,the superior performance has been transferred to agricultural image processing by combining them with traditional *** segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis,pest and disease identification,*** frst give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation *** we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learningbased ***,we outline their applications in agricultural image *** our literature,we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these *** robustness of the existing segmentation methods for processing complex images still needs to be improved urgently,and their generalization abilities are also *** particular,the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and *** this,segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation *** review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.

关键词： Semantic segmentation Agricultural images Deep learning Convolution neural networks

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Learned scalable video coding for humans and machines

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EURASIP JOURNAL ON image AND vIDEO processing 2024年第1期2024卷 41页

作者： Hadizadeh, Hadi Bajic, Ivan v. Simon Fraser Univ Sch Engn Sci 8888 Univ Dr Burnaby BC V5A 1S6 Canada

video coding has traditionally been developed to support services such as video streaming, videoconferencing, digital Tv, and so on. The main intent was to enable human viewing of the encoded content. However, with the advances in deep neural networks (DNNs), encoded video is increasingly being used for automatic video analytics performed by machines. In applications such as automatic traffic monitoring, analytics such as vehicle detection, tracking and counting, would run continuously, while human viewing could be required occasionally to review potential incidents. To support such applications, a new paradigm for video coding is needed that will facilitate efficient representation and compression of video for both machine and human use in a scalable manner. In this manuscript, we introduce an end-to-end learnable video codec that supports a machine vision task in its base layer, while its enhancement layer, together with the base layer, supports input reconstruction for human viewing. The proposed system is constructed based on the concept of conditional coding to achieve better compression gains. Comprehensive experimental evaluations conducted on four standard video datasets demonstrate that our framework outperforms both state-of-the-art learned and conventional video codecs in its base layer, while maintaining comparable performance on the human vision task in its enhancement layer.

关键词： video compression video analytics Scalable coding Deep learning Coding for machines

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An image-Based Transfer Learning Approach for Using In Situ processing Data to Predict Laser Powder Bed Fusion Additively Manufactured Ti-6Al-4v Mechanical Properties

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3D PRINTING AND ADDITIvE MANUFACTURING 2025年第1期12卷 48-60页

作者： Luo, Qixiang Shimanek, John D. Simpson, Timothy W. Beese, Allison M. Penn State Univ Dept Mat Sci & Engn University Pk PA USA Penn State Univ Dept Ind & Mfg Engn University Pk PA USA Penn State Univ Dept Mech Engn University Pk PA USA Penn State Univ Dept Mat Sci & Engn University Pk PA 16802 USA

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4v fabricated through laser powder bed fusion (PBF-LB) AM using in situ photodiode processing signals. Samples were fabricated under different processing parameters, varying laser powers and scan speeds for the purpose of probing a wide range of microstructure and property variations. Photodiode data were collected during fabrication, later to be arranged in image format and extracted to information-dense vectors by the transferal of deep convolutional neural network (DCNN) structures and weights pretrained on a large computer vision benchmark image database. The extracted features were then used to train and test a newly designed regression model for mechanical properties. Average cross-validation accuracies were found to be 98.7% (r(2) value of 0.89) for the prediction of ultimate tensile strength, which ranged from 900 to 1150 MPa in the samples studied, and 93.1% (r(2 )value of 0.96) for the prediction of elongation to fracture, which ranged from 0 to 17%. Thus, with high accuracy and hardware-accelerated inference speeds, we demonstrate that a transfer learning framework can be used to predict strength and ductility of metal AM components based on processing signals in PBF-LB, illustrating a potential route toward real-time closed-loop control and process optimization of PBF-LB in industrial applications.

关键词： machine learning computer vision deep convolution neural network laser powder bed fusion in situ processing monitoring

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Review of surface defect detection of steel products based on machine vision

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IET image processing 2023年第2期17卷 303-322页

作者： Tang, Bo Chen, Li Sun, Wei Lin, Zhong-kang Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state-of-art of vision-based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision-based surface defect inspection for steel products including hardware system, automated vision-based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre-processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

关键词： image segmentation feature extraction vision-based surface defect inspection technology image classification Industrial applications of IT production engineering computing quality control defect classification machine vision Optical, image and video signal processing steel products including hardware system Computer vision and image processing techniques steel Inspection and quality control image processing inspection review state-of-art visual inspection system Production engineering computing steel surface defect detection steel product surface defects composition computer vision automatic optical inspection reviews automated vision-based inspection method

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Hypercomplex Techniques in Signal and image processing Using Network Graph Theory: Identifying core research directions [Hypercomplex Signal and image processing]

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IEEE SIGNAL processing MAGAZINE 2024年第2期41卷 14-28页

作者： Alcayde, Alfredo ventura, Jorge Montoya, Francisco G. Univ Almeria Dept Engn Almeria 04120 Spain Univ Almeria Fac Engn Almeria 04120 Spain

This article aims to identify core research directions and provide a comprehensive overview of major advancements in the field of hypercomplex signal and image processing techniques using network graph theory. The methodology employs community detection algorithms on research networks to uncover relationships among researchers and topic fields in the hypercomplex domain. This is accomplished through a comprehensive academic database search and metadata analysis from pertinent papers. The article focuses on the utility of these techniques in various applications and the value of mathematically rich frameworks. The results demonstrate how optimized network-based approaches can determine common topics and emerging lines of research. The article identifies distinct core research directions, including significant advancements in image/video processing, computer vision, signal processing, security, navigation, and machine learning within the hypercomplex domain. Current trends, challenges, opportunities, and the most promising directions in hypercomplex signal and image processing are highlighted based on a thorough literature analysis. This provides actionable insights for researchers to advance this domain.

关键词： Surveys Computer vision Technological innovation Navigation Algebra image processing Quaternions

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image processing for machine vision applications

Image Processing for Machine Vision Applications

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作者： ASNANI, SORATH Polytechnic University of Turin

学位级别：博士

images are a vital part of our everyday life and image processing is the heart of all the modern technologies, including machine vision, artificial intelligence, robotics, deep learning. It would not be wrong to say that image processing is one of the many reasons for achieving success in any industrial domain, whether it be medical, food, textile, or any other automation industry. It is next to impossible to work in these domains without having sufficient knowledge and skills about image processing techniques. In this thesis document you will find the significance of image processing used in three diverse projects. Each one of the projects is described as a separate chapter in this document. The first project is focused on reducing the power consumption in OLED-based devices. Actually there are two main goals of this project, first one, as the name suggests, is to minimize the power consumed by an OLED device to display images, and the second goal is to simultaneously enhance the color contrasts in images. OLED display panels have become increasingly popular in recent years, thanks to their numerous advantages over the traditional LCD displays. Power consumption in OLED displays depends on the contents where as the backlight is responsible for power consumption in LCD displays. This image-dependent or content-dependent power consumption model of OLED displays have encouraged numerous researchers to create possibilities for reducing the power consumption in OLED-based devices. One such possibility has been explored in this Ph. D. research work. Another industrial application has been presented in the second part of the thesis document. It is a part of the "Food Digital Monitoring" project, funded by Regione Piemonte. The major aim of this project is to identify the healthy and contaminated hazelnuts by using fluorescence and spectral imaging techniques. Two types of contamination are discussed in this work, one, caused by bacterial and fungal infections, called "rot

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Integrated vision-based seam tracking system for robotic laser welding of curved closed square butt joints

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INTERNATIONAL JOURNAL OF ADvANCED MANUFACTURING TECHNOLOGY 2025年第7-8期137卷 3387-3399页

作者： Nilsen, Morgan Sikstrom, Fredrik Univ West Dept Engn Sci S-46180 Trollhattan Sweden

This study presents a vision-based closed-loop tracking system designed specifically for robotic laser beam welding of curved and closed square butt joints. The proposed system is compared against 11 existing solutions reported in the literature, which employ various sensor principles for the same application. The system employs a non-contact, non-intrusive machine vision approach, seamlessly integrated into the laser beam welding head to mitigate challenges associated with sensor forerun. Key features include an off-axis LED illumination, an optical filter, and a movable actuator, facilitating real-time image processing and closed-loop control during the welding process. Experimental validation was conducted on stainless-steel plates with complex closed square butt joints. The system achieved a mean absolute joint-to-beam offset of 0.14 mm across four test cases, with a maximum offset of 0.85 mm, demonstrating its robustness and precision. Comparative analysis underscores the proposed method's advantages, showcasing its potential for industrial applications in laser beam welding of geometrically challenging joints.

关键词： Robotic laser welding Seam tracking machine vision image processing Non-contact sensing Automatic control

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HDR vision sensor with neuro-memristive skin detection for edge computing

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND vision 2024年第6期41卷 1009-1018页

作者： Paissan, Francesco Lecca, Michela Passerone, Roberto Farella, Elisabetta Gottardi, Massimo Fdn Bruno Kessler I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

Human skin classification is an essential task for several machine vision applications such as human -machine interfaces, people/object tracking, and classification. In this paper, we describe a hybrid CMOS/memristor vision sensor architecture embedding skin detection over a wide dynamic range. In -sensor RGB to rg -chromaticity colorspace conversion is executed on -the -fly through a pixel -level automatic exposure time control. Each pixel of the array delivers two pre -filtered analog signals, the r and g values, suitable for being efficiently classified as skin or non -skin through an analog memristive neural network (NN), without the need for any further signal processing. Moreover, we study the NN performance and theorize how it should be added in the hardware. The skin classifier is organized in an array of column -level memristor-based NN to exploit the nano -scale device characteristics and non-volatile analog memory capabilities, making the proposed sensor architecture highly flexible, customizable for various use -case scenarios, and low -power. The output is a skin bitmap that is robust against variations of the illuminant color and intensity. (c) 2024 Optica Publishing Group

关键词： CMOS cameras Edge detection image sensors machine vision Neural networks Signal processing

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On-machine dimensional inspection: machine vision-based approach

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INTERNATIONAL JOURNAL OF ADvANCED MANUFACTURING TECHNOLOGY 2024年第1期131卷 393-407页

作者： Taatali, Abdelali Sadaoui, Sif Eddine Louar, Mohamed Abderaouf Mahiddini, Brahim Ecole Mil Polytech Lab Tech Avancees Fabricat & Controle Algiers 16111 Algeria Ecole Mil Polytech Lab Dynam Syst Mecan Algiers 16111 Algeria

The contemporary industry has witnessed a significant transformative development with the integration of artificial intelligence (AI) in various industrial systems, resulting in an enhanced automation for heightened productivity and efficiency. However, mastering this level of automation can be challenging for some applications, such as manufacturing inspection, which can be delicate while maintaining a precise cadence for an in-line manufacturing scale. In this paper, a systematic machine vision-based approach for on-machine inspection is proposed in order to automate and improve inspection process towards computer numerical control (CNC) machined parts. The approach incorporates remapping algorithm and image processing operations to accurately extract desired features. Subsequently, these features will undergo dimensional inspection based on their generated point clouds. Tests were applied on a sample part using a complementary metal-oxide-semiconductor (CMOS) camera mounted on the spindle of 5-axis CNC machining center. The paper explores numerous aspects related to different stages of the approach and their impact on the resulting inspected features evaluations. It also highlights significant findings regarding critical factors for conducting well-structured experiments at various stages. Promising results have shown the significance of the presented work regarding industrial automation technology, ultimately improving manufacturing efficiency throughout the production line.

关键词： Dimensional inspection On-machine inspection machine vision image processing Point cloud

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Learning-based light field imaging: an overview

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EURASIP JOURNAL ON image AND vIDEO processing 2024年第1期2024卷 12页

作者： Mahmoudpour, Saeed Pagliari, Carla Schelkens, Peter Vrije Univ Brussel VUB Dept Elect & Informat ETRO Pleinlaan 2 B-1050 Brussels Belgium Imec Kapeldreef 75 B-3001 Leuven Belgium Inst Mil Engn PGEE PGED IME Rio De Janeiro Brazil

Conventional photography can only provide a two-dimensional image of the scene, whereas emerging imaging modalities such as light field enable the representation of higher dimensional visual information by capturing light rays from different directions. Light fields provide immersive experiences, a sense of presence in the scene, and can enhance different vision tasks. Hence, research into light field processing methods has become increasingly popular. It does, however, come at the cost of higher data volume and computational complexity. With the growing deployment of machine-learning and deep architectures in image processing applications, a paradigm shift toward learning-based approaches has also been observed in the design of light field processing methods. various learning-based approaches are developed to process the high volume of light field data efficiently for different vision tasks while improving performance. Taking into account the diversity of light field vision tasks and the deployed learning-based frameworks, it is necessary to survey the scattered learning-based works in the domain to gain insight into the current trends and challenges. This paper aims to review the existing learning-based solutions for light field imaging and to summarize the most promising frameworks. Moreover, evaluation methods and available light field datasets are highlighted. Lastly, the review concludes with a brief outlook for future research directions.

关键词： Light fields Depth estimation image reconstruction Compression machine learning Deep learning

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