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2nd IEEE-Industrial-Electronics-Society Annual On-Line Conference (ONCON)

作者： Bagchi, Sourav Aditya, Janumpally Varun Kumari, Sneha Dhanraj, Malla Jenamani, Mamata Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

ISBN: (纸本)9798350357974

Manual visual assessment of mangoes has been problematic for the agriculture sector because of its time-consuming nature and inconsistent evaluation and sorting methods. The advent of automated flaw identification using computer vision and machine learning offers a notable shift and improvement in the visual inspection process. A common issue with mangoes is the presence of dark patches, indicative of disease or rot, which negatively affect the appearance and quality of the fruit. This paper introduces a framework using computer vision which utilizes image analysis and machine learning methods to identify these dark spots, taking into account the mangoes' texture. The proposed framework has a simplified configuration and tuning process, enhancing its ease of deployment in real-world applications. This innovation aligns with the advancements in integrating cutting-edge technologies to optimize efficiency and consistency in agricultural practices, thereby contributing to the evolution of smart agriculture and addressing the challenges and opportunities presented by the next wave of industrial revolution.

关键词： Computer vision System Dark Patches detection image processing Local Binary Pattern (LBP) machine Learning Random Forest SVM Classifier Grading

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Bio-Inspired Electronic Eyes and Synaptic Photodetectors for Mobile Artificial vision

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2022年第2期1卷 76-87页

作者： Choi, Changsoon Seung, Hyojin Kim, Dae-Hyeong Seoul02792 Korea Republic of Seoul08826 Korea Republic of School of Chemical and Biological Engineering Institute of Chemical Processes Seoul National University Seoul08826 Korea Republic of

Conventional imaging and data processing devices are not ideal for mobile artificial vision applications, such as vision systems for drones and robots, because of the heavy and bulky multilens optics in the camera modules. Furthermore, the physically isolated image data processing units of conventional systems induce large power consumption and data latency. For mobile artificial vision applications, electronic eyes, including neuromorphic ones, have been developed inspired by biological eyes and neural networks. Here, we summarize the development of such bio-inspired electronic eyes and synaptic photodetectors (PDs). Bio-inspired electronic eyes, typically consisting of curved image sensor arrays, enable aberration-free imaging and module size miniaturization in addition to other advantageous optical features, such as wide field-of-view and deep depth-of-field. Furthermore, photodetecting devices with synaptic properties can efficiently enhance image contrast because of photon-triggered synaptic plasticity. Therefore, the signal-to-noise ratio of the acquired image can be enhanced, which facilitates efficient image recognition for machine vision. A brief summary of the remaining challenges and prospects concludes this review. © 2022 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Cameras

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Detection and diabetic retinopathy grading using digital retinal images

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND applications 2023年第2期7卷 426-458页

作者： Malhi, Avleen Grewal, Reaya Pannu, Husanbir Singh Bournemouth Univ Poole England Aalto Univ Dept Comp Sci Espoo Finland Thapar Inst Engn & Technol Patiala India

Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient's vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments;one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease.

关键词： machine learning image processing Diabetic retinopathy Exudates Microaneurysms

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FruitQ: a new dataset of multiple fruit images for freshness evaluation

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MULTIMEDIA TOOLS AND applications 2024年第4期83卷 11433-11460页

作者： Abayomi-Alli, Olusola O. O. Damasevicius, Robertas Misra, Sanjay Abayomi-Alli, Adebayo Kaunas Univ Technol Dept Software Engn LT-44249 Kaunas Lithuania Inst Energy Technol Dept Appl Data Sci N-1777 Halden Norway Fed Univ Agr Dept Comp Sci Abeokuta 110124 Nigeria

Application of artificial intelligence methods in agriculture is gaining research attention with focus on improving planting, harvesting, post-harvesting, etc. Fruit quality recognition is crucial for farmers during harvesting and sorting, for food retailers for quality monitoring, and for consumers for freshness evaluation, etc. However, there is a lack of multi-fruit datasets to support real-time fruit quality evaluation. To address this gap, we present a new dataset of fruit images aimed at evaluating fruit freshness, which addresses the lack of multi-fruit datasets for real-time fruit quality evaluation. The dataset contains images of 11 fruits categorized into three freshness classes, and five well-known deep learning models (ShuffleNet, SqueezeNet, EfficientNet, ResNet18, and MobileNet-V2) were adopted as baseline models for fruit quality recognition using the dataset. The study provides a benchmark dataset for the classification task, which could improve research endeavors in the field of fruit quality recognition. The dataset is systematically organized and annotated, making it suitable for testing the performance of state-of-the-art methods and new learning classifiers. The research community in the fields of computer vision, machine learning, and pattern recognition could benefit from this dataset by applying it to various research tasks such as fruit classification and fruit quality recognition. The study achieved impressive results with the best classifier being ResNet-18 with an overall best performance of 99.8% for accuracy. The study also identified limitations, such as the small size of the dataset, and proposed future work to improve deep learning techniques for fruit quality classification tasks.

关键词： fruit freshness evaluation fruit decay detection precision agriculture image processing computer vision

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Contextual Transformer Networks for Visual Recognition

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第2期45卷 1489-1500页

作者： Li, Yehao Yao, Ting Pan, Yingwei Mei, Tao JD Explore Acad Beijing 101111 Peoples R China

Transformer with self-attention has led to the revolutionizing of natural language processing field, and recently inspires the emergence of Transformer-style architecture design with competitive results in numerous computer vision tasks. Nevertheless, most of existing designs directly employ self-attention over a 2D feature map to obtain the attention matrix based on pairs of isolated queries and keys at each spatial location, but leave the rich contexts among neighbor keys under-exploited. In this work, we design a novel Transformer-style module, i.e., Contextual Transformer (CoT) block, for visual recognition. Such design fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation. Technically, CoT block first contextually encodes input keys via a 3 x 3 convolution, leading to a static contextual representation of inputs. We further concatenate the encoded keys with input queries to learn the dynamic multi-head attention matrix through two consecutive 1 x 1 convolutions. The learnt attention matrix is multiplied by input values to achieve the dynamic contextual representation of inputs. The fusion of the static and dynamic contextual representations are finally taken as outputs. Our CoT block is appealing in the view that it can readily replace each 3 x 3 convolution in ResNet architectures, yielding a Transformer-style backbone named as Contextual Transformer Networks (CoTNet). Through extensive experiments over a wide range of applications (e.g., image recognition, object detection, instance segmentation, and semantic segmentation), we validate the superiority of CoTNet as a stronger backbone.

关键词： Transformers Convolution Visualization Computer architecture Task analysis image recognition Object detection Transformer self-attention vision transformer image recognition

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Deep Learning for Automatic vision-Based Recognition of Industrial Surface Defects: A Survey

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IEEE ACCESS 2023年 11卷 43370-43423页

作者： Prunella, Michela Scardigno, Roberto Maria Buongiorno, Domenico Brunetti, Antonio Longo, Nicola Carli, Raffaele Dotoli, Mariagrazia Bevilacqua, Vitoantonio Polytech Univ Bari Dept Elect & Informat Engn DEI I-70126 Bari Italy Apulian Bioengn Srl I-70026 Modugno Bari Italy Comau SpA I-10095 Turin Italy

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

关键词： Feature extraction Transfer learning Deep learning Artificial intelligence Inspection Manuals image recognition Computer vision Autonomous systems Generative adversarial networks Artificial vision auto-encoder automatic recognition feature attention mechanism convolutional neural network deep learning explainable artificial intelligence generative-adversarial network industrial surface defects transfer learning

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning 1

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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1st International Conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Anees, H. Agrawal, Pooja School of Robotics Defence Institute of advanced technology Pune India

ISBN: (纸本)9798350355611

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies. © 2025 IEEE.

关键词： Steering

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Breaking Through Color Casts: Enhancing image Fidelity with machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Reddy, Choppa Jeevan Sai Akshay, Bavani Bhawane, Bange Reddy, Busireddy Parvathammagari Srikanth Satish, Addanki Sree Vidyanikethan Engineering College Tirupati India School of Engineering Mohan Babu University Tirupati Dept. of Ece India

ISBN: (纸本)9798350375237

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques. © 2024 IEEE.

关键词： image enhancement

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A Comparative Study of Deep Learning Algorithms for Glaucoma Classification Using Retinal images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th International Conference on Data Science, machine Learning and applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. There have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. The study uses eye fundus images to classify glaucoma patients using three distinct Deep Learning techniques: Convolutional neural network, Visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. This research compares and analyses the performance of various architectures using the aforementioned techniques. The CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness machine learning Visual geometry group 16 Convolutional neural network Global context network

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Deep learning-based solutions for electron microscopy image analysis

Deep learning-based solutions for electron microscopy image ...

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作者： Nguyen, Nguyen Phuoc University of Missouri

学位级别：博士

Electron microscopy (EM) enables capturing high resolution images of very small structures in biological and non-biological specimens such as membrane proteins, viruses, subcellular structures, nanoparticles, or material surfaces. Electron microscopy plays a critical role in research, development, and diagnosis in many applications of biological, physical, chemical and material sciences. Thanks to advances in instrumentation, electron microscopy generates large amounts of complex data that is no longer feasible to analyze manually. There is a growing need for development of computational methods and tools for automated analysis of electron microscopy data generated for variety of research fields. Recent advances in artificial intelligence and machine learning, particularly in deep learning have revolutionized image processing and computer vision. In this work, we explored deep learning guided image processing and computer vision solutions to address the growing high-performance processing needs of image data acquired using electron microscopy. The proposed solutions involved novel multi-step, 2D/3D fusion approaches to address the unique challenges of complex, low-contrast, noisy electron microscopy imagery; and selfsupervised, semi-supervised, or meta-learning schemes to address the challenges caused by lack of or limited amounts of labeled training data. These image analysis solutions were used for detection, segmentation, and quantification of various biological structures of interest such as proteins, viruses, mitochondrial or neural structures; and non-biological structures of interest such as carbon nanotube forests. Experiments conducted on the proposed methods showed robust and promising results towards automated, objective, and quantitative analysis of electron microscopy image data, that is of great value for biology, medicine, and material science applications.

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