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A machine Learning-based Approach for Automatic Grading and Quality inspection of Indian Mangoes

A Machine Learning-based Approach for Automatic Grading and ...

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On-Line conference (ONCON), Industrial Electronics Society Annual

作者： Sourav Bagchi Janumpally Varun Aditya Sneha Kumari Malla Dhanraj Mamata Jenamani Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India

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.

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Micro vision-based Sharpening Quality Detection of Diamond Tools 15th

Micro Vision-based Sharpening Quality Detection of Diamond T...

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15th International conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Xue, Wen Zhao, Chenyang Fu, Wenpeng Du, Jianjun Yao, Yingxue Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031138416;9783031138409

Ultra-precision grinding is the last critical step in diamond tool machining. At present, most of the tool quality detection methods in the grinding process are offline measurement, which will reduce production efficiency. The general tool condition monitoring (TCM) method is designed for the use of the tool for processing and production, and there are relatively few studies on the detection in the process of producing diamond tools. Referring to the general TCM method, this paper adopts the machine vision detection method based on deep learning to realize the on-machine detection of the grinding quality of diamond tools. The method proposed in this paper is optimized for the recognition of small-sized defect targets of diamond tools, and the impact of less data on the training of the recognition network is improved. First, an imaging system is built on an ultraprecision grinder to obtain information suitable for detecting defective targets. These tool images are then used to train an optimized object recognition network, resulting in an object recognition network model. Finally, the performance of the network on the task of diamond tool defect recognition is verified by experiments. The experimental results show that the method proposed in this paper can achieve an average accuracy of 87.3% for diamond tool detection and a 6.0% improvement in position accuracy.

关键词： Diamond tools visual inspection Tool condition monitoring

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R-CNN based automated visual inspection system for engine parts quality assessment 15

R-CNN based automated visual inspection system for engine pa...

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15th International conference on Quality Control by Artificial vision

作者： Leger, Antoine Le Goic, Gaetan Fauvet, Eric Fofi, David Kornalewski, Remi Univ Burgundy ImVia Lab 64 Rue Sully Dijon France F2J REMAN Route Neuilly Chaumont France

ISBN: (数字)9781510644274

ISBN: (纸本)9781510644274

In this paper, we attempt to answer to a quality control problem in the context of an industrial serial production of lower plates (wheel suspensions) for the automotive industry. These frame parts are produced by a 2000-ton stamping machine that can reach 1800 parts per hour. The quality of these parts is assessed by a visual quality control operation. This operation is time-consuming. Moreover, many factors can affect its performance, as the attention of the operators in charge, or a too rapid inspection completion time, and non-detection defects lead to high supplementary costs. To answer this issue and automate this process operation, a system based on a vision system coupled to a pre-trained Convolutional Neural Networks (Mask R-CNN)(1) has been designed and implemented. In addition, an artificial enlargement of the reference image base is proposed to improve the robustness of the identification, and reduce the sensitivity of the results to potential imaging artefacts due to non-controlled environments factors such as overexposure, blur, shadows or oil fog.

关键词： CNN Deep Learning Quality control machine vision Computer vision Mask R-CNN

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AI-Enhanced visual inspection Systems for Robust Detection of Product Packaging Defects in Manufacturing Environments

AI-Enhanced Visual Inspection Systems for Robust Detection o...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication conference (UEMCON)

作者： Imran Ahmed Muftooh Ur Rehman Siddiqi Misbah Ahmad School of Computing and Information Sciences Anglia Ruskin University Cambridge UK School of Engineering and Technology Aston University Birmingham UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

AI-enhanced visual inspection systems are important for ensuring product quality and customer satisfaction, particularly in automated manufacturing environments where defects can disrupt supply chains and lead to contamination risks. This study applies the YOLOv7 object detection algorithm for detecting packaging defects such as scratches holes, and deformations in jar lids. The dataset contains 1859 jar lids, on average 11 per image, categorized into intact (962) versus damaged (897) jar lids. After optimizing for class imbalance and performance, the system achieved precision rates of $91.7 \%$ overall and $3.5 \%$ for the ‘damaged’ class, demonstrating robustness in challenging environments. This work presents a scalable AI-based solution to improve defect detection efficiency in manufacturing.

关键词： visualization Supply chains Object detection Packaging inspection Mobile communication Robustness Product design Manufacturing Quality assessment

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automated Posture Analysis for the Assessment of Sports Exercises 22

Automated Posture Analysis for the Assessment of Sports Exer...

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12th Hellenic conference on Artificial Intelligence (SETN)

作者： Pardos, Antonis Tziomaka, Melina Menychtas, Andreas Maglogiannis, Ilias Univ Piraeus Dept Digital Syst Piraeus Greece

ISBN: (纸本)9781450395977

Numerous studies in the medical field correlate the maintenance of human posture in static and dynamic situations with the muscleskeletal health. One of the most widely used methods for assessing human posture is through visual inspection by professionals. However, this observational assessment process requires the presence of a field expert performing a time-consuming manual analysis. Hence, a reliable automatic posture evaluation system would be of great help for professionals to detect postural misalignments. In the recent years, significant progress has been achieved in pose estimation through state-of-the-art deep learning techniques, competent to estimate human body landmarks fast and accurately from RGB images. In this paper, we describe a methodology scheme to estimate human posture and detect postural misalignments in static and dynamic exercises in real-time. The MediaPipe Pose algorithm is employed to detect human pose and the vector geometry of the pose is evaluated to detect postural misalignments. Furthermore, in order to not limit the applications of this work by preselecting rule parameters for only a certain set of exercises, the rule parameters of any form of exercise are automatically extracted from an example of a single correct execution through machine learning. The datasets of the videos utilized in this work were provided and annotated by a clinical exercise physiologist.

关键词： MediaPipe Pose Computer vision Deep Learning machine Learning

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Pothole Detection under Diverse Conditions using Object Detection Models

Pothole Detection under Diverse Conditions using Object Dete...

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International conference on Image Processing and vision Engineering (IMPROVE)

作者： Hassan, Syed Ibrahim O'Sullivan, Dympna Mckeever, Susan Technol Univ Dublin Dept Comp Sci Dublin Ireland

ISBN: (纸本)9789897585111

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalisable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles and resolutions. In this paper we present our approach to building a generalized learning model for pothole detection. We apply four datasets that contain a range of image and environment conditions. Using the Faster RCNN object detection model, we demonstrate the extent to which pothole detection models can generalise across various conditions. Our work is a contribution to bringing automated road maintenance techniques from the research lab into the real-world.

关键词： Object Detection Pavement inspection Deep Learning machine Learning

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Intelligent Quality Control System for Product Manufacturers through ML

Intelligent Quality Control System for Product Manufacturers...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International conference on

作者： Sagar Nikam Neelima Kolhare Electronics and Telecommunication College Of Engineering Pune Pune India

One of the most important components in ensuring product quality before it reaches the market is industrial inspection. The visual system, including human vision, machine vision, or a mix of both, can be used to carry out the inspection tasks. In this research, we present a method that can regulate the product quality of press parts. visual inspection systems are used to accomplish quality control by identifying acceptable parts from rejected goods. Complete quality control of the raw materials is a requirement for large-scale production enterprises. The primary goals are to create an image processing system that can assess a part's dimension and determine whether it should be accepted or rejected. Calculating the pixel value will yield the part's dimensions. The result shows its possibility to be used as automated visual inspection system.

关键词： visualization machine vision Quality control inspection visual systems Product design Raw materials

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Extracting Color Name Features Utilized for Skin Disease Characterization and Comparing It to Other Representations Describing the ABCD Dermatological Criteria for Melanoma inspection 7th

Extracting Color Name Features Utilized for Skin Disease Ch...

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7th and 8th International conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Daghrir, Jinen Tlig, Lotfi Bouchouicha, Moez Moreau, Eric Litaiem, Noureddine Zeglaoui, Faten Sayadi, Mounir ISITCom Université de Sousse Hammam Sousse4011 Tunisia ENSIT Laboratory SIME Université de Tunis Tunis Tunisia Aix Marseille Univ Université de Toulon CNRS LIS Toulon France Faculty of Medicine of Tunis Department of Dermatology Charles Nicolle Hospital University of Tunis El-Manar Tunis Tunisia

ISBN: (纸本)9783031374951

Recent development in image resolution has led to the excessive use of medical images in creating automated systems for inspecting and early detection of deadly diseases. Some computer vision diagnosis systems has focused on the inspection and analysis of skin cancers, such as melanoma. It is considered as one of the most fatal skin condition if it is caught at an advanced stage. Thus, it is important to regularly check every skin lesion for an early detection of melanoma. Many systems have been introduced in the last years. They mainly rely on visual features describing the color, border and texture to inspect the malignancy of skin lesions. The performance of such systems is impressive but still lacks the integration of many other different clues invented by dermatologist. Studying the color variegation can shed the light on some criteria already adopted by doctors of malignant lesions. Thus, extracting the different colors skin lesion contain is of high relevance. This paper presents a novel method for extracting and quantifying the different colors in a skin lesion in a supervised way that will ensure high specificity and sensitivity in classifying different skin conditions simultaneously. This paper aims to prove the effectiveness of our proposed method by making a comparative analysis of different state-of-the-art methods held on the ISIC 2017 and SD 198 datasets. Moreover, we employ a collected dataset to explore the performance of our proposed method. The application of our hand-crafted color features results in better classification performance of different skin illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Anomaly Detection in Industrial Quality Control with Computer vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： A. Jenita Mary V. Umesh Prerana Nilesh Khairnar R Sahana Sangeethaa SN Shanthi H J Department of Computer Science and Applications SRM Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Chennai India Department of IT Ksou Mysore Mysuru India Department of Electronics and Telecommunication Engineering Visvesvaraya Institute of Technology Chincholi Nashik India Department of Artificial Intelligence and Machine Learning Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Department of Computer Science & Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Training Deep learning Adaptation models Accuracy Computational modeling Quality control Production inspection Convolutional neural networks Anomaly detection

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11th International conference on Robotics, vision, Signal Processing and Power Applications, RoViSP 2021

11th International Conference on Robotics, Vision, Signal Pr...

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11th International conference on Robotics, vision, Signal Processing and Power Applications, RoViSP 2021

ISBN: (纸本)9789811681288

The proceedings contain 166 papers. The special focus in this conference is on Robotics, vision, Signal Processing and Power Applications. The topics include: Development of COVID-19 Prediction Models from Chest X-Ray Using Transfer Learning;behavioural Study of Constrained Convolutional Neural Network on Image Splicing Classification of Various Datasets;survey on Loss Function for Face Recognition;a Maximum Power Point Tracking Using Levy Particle Swarm;Sensing and Control of Autonomous Driverless Car on Road (SCAD);impact of Similarity Measure Functions on the Performance of Coherent Filtering Detection;UAV Control System with Time to Collision (TTC) Prediction Capability;AFSA-SLnO Variants for Enhanced Global Optimization;A Fast and Flexible Turret Based automated vision inspection (AVI) to Inspect 6 Sides of Small Discrete Components;development of Morphological Image Processing Learning Module for Mobile Application;Design a RFID Tag Antenna for Wearable Application System;automatic Detection of Cerebral Ischemia (Cerebral Occlusion) by Using Frame Differencing Method;a Hybrid Deep Learning Model for Face Sketch Recognition;Point Cloud Generation with Low-Cost Camera for visual SLAM;Development of Drowning Detection Algorithm for USV;latest Trends of Integration of Gas Leakage and Fire Detection Using IoT: A Survey;Safety Device for Runners (SDR) Using Arduino Microcontroller;analytical Subdomain Model for Double-Stator Permanent Magnet Synchronous machine;Dorsal Hand Vein Segmentation Using Vein-Generative Adversarial Network (V-GAN) Model;handling Illusive Text in Document to Improve Accuracy of Plagiarism Detection Algorithm;PQ Control Strategy in Single-Phase Inverter for Grid-Connected Photovoltaic Energy System Under Linear and Nonlinear Loads;preface;inchworm-Inspired Semi-autonomous Pole-Climbing Robot.

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