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A comprehensive review on soil classification using deep learning and computer vision techniques

MULTIMEDIA TOOLS AND applications 2021年第10期80卷 14887-14914页

作者： Srivastava, Pallavi Shukla, Aasheesh Bansal, Atul GLA Univ Dept Elect & Commun Engn Mathura 281406 India

Soil classification is one of the major affairs and emanating topics in a large number of countries. The population of the world is rising at a majorly rapid pace and along with the increase in population, the demand for food surges actively. Typical techniques employed by the farmers are not adequate enough to fulfill the increasing requirements and therefore they have to hinder the cultivating soil. For proper crop yield, farmers should be aware of the correct soil type for a particular crop, which affects the increased demand for food. There are various laboratory and field methods to classify soil, but these have limitations like time and labor-consuming. There is a requirement of computer-based soil classification techniques which will help farmers in the field and won't take a lot of time. This paper talks about different computer-based soil classification practices divided into two streams. First is image processing and computer vision-based soil classification approaches which include the conventional image processing algorithms and methods to classify soil using different features like texture, color, and particle size. Second is deep learning and machine learning-based soil classification approaches, such as CNN, which yields state-of-the-art results. Deep learning applications mostly diminish the dependency on spatial-form designs and preprocessing techniques by facilitating the end-to-end process. This paper also presents some databases created by the researchers according to the objective of the study. Databases are created under different environmental and illumination conditions, using different appliances such as digital cameras, digital camcorder, and a smartphone camera. Also, evaluation metrics are briefly discussed to layout some graded measures for differentiation. This review serves as a brief guide to new researchers in the field of soil classification, it provides fundamental understanding and general knowledge of the modern state-of-the-art

关键词： Soil classification Deep learning Convolutional neural network Computer vision Soil science

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MFA-DAF: Unsupervised Multimodal Medical image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Xinyu Xie Xiaozhi Zhang Dongping Xiong Lijun Ouyang School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality.

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Lung Pathology Using Artificial Intelligence Analysis of Ultrasound images: A Survey

Lung Pathology Using Artificial Intelligence Analysis of Ult...

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Sustainable Technology and Engineering (i-COSTE), International Conference on

作者： Ali Alwadi Girija Chetty Mohammad Alwadi Jawad Alnaimi Amjad Gawanmeh Khaled Zuhair Said Mahmoud Faculty of Science and Technology University of Canberra Canberra Australia Department of Computer Studies Arab Open University Amman Jordan College of Engineering & IT University of Dubai Dubai United Arab Emirates State University of New York at Binghamton Hillsboro Oregon United States of America

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

This paper first studies and analyses the Artificial Intelligence algorithms that can potentially be used to identify various diseases from medical images and scans. Then researches the related work in this area to find a suitable machine Learning model that can be used to identify Interstitial Syndrome in Lung Ultra Sound images. By researching similar applications of the latest machine Learning algorithms, this team of researchers are adopting a vision Transformer system developed on a Convolutional Neural Network-based algorithm. The later has proven to be efficient in the area of UltraSound image processing in general and more flexible to configure and parameterize in the training process. The vision Transformer system is a segmentation-based model that divides the input image to smaller subset of images of the same size. By merging the adjacent small patches into bigger layers, bigger windows to perform self-attention on, the system extracts the points of interest out of these frames.

关键词： Training Surveys image segmentation Computer vision Ultrasonic imaging machine learning algorithms Lungs Transformers Convolutional neural networks Diseases

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Autofocus optical imaging system based on image processing 14

Autofocus optical imaging system based on image processing

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14th International Conference on Information Optics and Photonics, CIOP 2023

作者： Teng, Teng Chen, Guan Xiaofei, Wang Hua, Fan Dongjie, Ju Zhaojuan, Chi Jinan250000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510671744

Autofocus optical imaging systems are widely used in industrial inspection, medical diagnosis, drone photography, machine vision and other fields. image-based autofocus algorithms typically consist of two key steps: image sharpness evaluation and search strategy. In this study, we propose an advanced image-based autofocus algorithm specifically designed for industrial image measurement. In order to improve the accuracy of the clarity assessment, we improve the existing method by introducing Gaussian filtering and threshold controlled Laplace operators. This improvement effectively reduces the effects of noise and light intensity changes, improving the reliability of clarity measurements. In addition, we propose a novel distance adjustment strategy combining coarse and fine tuning as part of the search strategy. This strategy reduces the interference of local peaks, allowing the algorithm to accurately identify the best image focus. The proposed image-based autofocus algorithm has several advantages, e.g. high focusing accuracy, high repeatability, and stability under light intensity changes2. These advantages make it ideal for industrial image measurement applications. To verify the effectiveness of our method, we build an experiment setup in a lab using ADLINK PCI-9114-DG board and a custom-designed lens. The results show that the sensitivity and performance of the autofocus system meet certain requirements. In summary, the image-based autofocus algorithm that we developed enables accurate and reliable focusing. It overcomes some challenges such as noise and light intensity changes to ensures optimal image focus, and improves overall image quality. The successful implementation of our autofocus system can benefit industrial inspection, medical diagnostics and many other applications. © 2023 SPIE.

关键词： Diagnosis

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Novel segmentation method and measurement system for various grains with complex touching

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 202卷

作者： Liang, Ning Sun, Sashuan Yu, Jiajia Taha, Mohamed Farag He, Yong Qiu, Zhengjun Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou Peoples R China Minist Agr & Rural Affairs Key Lab Spect Sensing Hangzhou Peoples R China Zhejiang Inst Mech & Elect Engn Hangzhou Peoples R China Arish Univ Fac Environm Agr Sci Dept Soil & Water Sci North Sinai Egypt Zhejiang Univ Zhongyuan Inst Zhengzhou Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China

Thousand kernel weight is one of the essential indicators for evaluating grain quality. Manual work and me-chanical counter are traditional means for thousand kernel weight measurement, but they cannot meet the re-quirements of modern applications in efficiency and intelligence. machine vision is a powerful and promising technology that can replace traditional measurement means in many fields. Grain segmentation and counting are significant challenges in thousand kernel weight measurement relying on machine vision. This study proposed a novel segmentation method for complex-touching grains of different species, shapes, and sizes. In the method, three specialized algorithms were applied to segment non-touching, simple-touching, and complex-touching grains in the captured images to improve segmentation accuracy. The K-means clustering algorithm was used to extract non-touching grains, and a layered watershed algorithm was created to segment simple-touching grains. A splitting line detection algorithm was innovatively designed by the determination of concave corner point sets, line detection, and line extension to realize accurate segmentation of complex-touching grains. Error correction was carried out at the end of the proposed method to reduce under-segmentation and over -segmentation. Furthermore, a thousand kernel weight measurement system was developed based on the pro-posed segmentation method. The results for 1000 images containing 640 thousand grains showed that the developed system achieved an average measurement accuracy of 99.65 % and an average measurement time of 1.54 s. The measurement accuracy could be further improved to 100 % by the manual correction. The developed system was distinctly superior in accuracy, efficiency, and application scope for the thousand kernel weight measurement of various grains with complex touching.

关键词： Grain counting Target segmentation Watershed algorithm Harris corner detection image processing

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Industrial objects recognition in intelligent manufacturing for computer vision

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INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2022年第4期10卷 401-415页

作者： Jain, Tushar Meerut Inst Engn & Technol Mech Engn Meerut Uttar Pradesh India Natl Inst Technol Kurukshetra Kurukshetra Haryana India

Purpose The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts. Design/methodology/approach Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum. Findings One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that

关键词： Industrial objects recognition Fuzzy image classification Generic fourier descriptor Artificial neural network Intelligent manufacturing Robot vision

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Autonomous Object Detection and Counting using Edge Detection and image processing Algorithms

Autonomous Object Detection and Counting using Edge Detectio...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Swati B. Patil Jay Chandrakant Shimpi Archana Girish Tanawade Pranali Gajanan Chavan Vrushali Shrimant Tandulkar Information Technology Vishawakarma Institute of Information Technology Pune India T and T infra Ltd Pune India

machine vision applications are commonly utilised in manufacturing lines as low cost, high precision measuring devices. Output facilities can accomplish high production numbers without mistakes thanks to these solutions that offer contactless control and measurement. A camera may be used to carry out machine vision tasks including product counting., error checking., and dimension measuring. This study makes a recommendation for a vision system application that can do inanimate object item enumeration. The recommended solution uses Otsu thresholding., Hough transformations., edge detection methods., and other image processing algorithms to accomplish automatic counting without taking into account the kind or colour of the product. The system primarily uses one camera. The general idea is to get image with balanced contrast., brightness and appropriate HSV values in it. A picture of the items being captured using camera using android device., and different image processing techniques are then applied to the picture. Further., a real-time machine vision programme was deployed and took photos taken from an actual experimental setup. The practical experiments conducted have shown that the suggested technique yields quick., precise., and trustworthy results based on the comparative study of various detection techniques.

关键词： machine learning algorithms Thresholding (Imaging) image edge detection machine vision Measurement uncertainty Production Object detection

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GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

GLSI Texture Descriptor Based on Complex Networks for Music ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Coca Salazar, Andres Eduardo Fed Univ Technol Parana UTFPR TD Comp Engn Coordinat COENC Toledo PR Brazil

ISBN: (纸本)9781665488679

The texture classification of an image is related to an important musical attribute, the music genre. This relationship is depicted in the visual representation of the audio signal, called as spectrogram. In this paper, we propose a new Music Genre Classification (MGC) system that processes the spectrogram texture using the Gray Level and Structural Information (GLSI) descriptor, and represents the interconnection between the descriptor codes through complex networks. The GLSI descriptor is an improvement of the CLBP (Completed Local Binary Pattern) descriptor, which quantifies the texture of an image with three codes: signal (CLBP-S), magnitude (CLBP-M), and central (CLBP-C). By transforming the CLBP-C code, GLSI adds macro-structural information. The network nodes represent the descriptor codes, and the respective edges, the relationship according to the horizontal and vertical consecutive condition. We defined two representations for the nodes: 1) individual code node, obtaining the G(s), G(m) and G(g) networks, and 2) triple code node, obtaining the G(smg) network. For the experimental stage, we used the GTZAN dataset, three types of spectrograms: conventional, mel-spectrogram and gammatonegram;and mining with network topological measures. For each type of spectrogram, we performed three experiments according to feature vector combinations, such as measures of: 1) G(s), G(m) and G(g), 2) G(smg), and 3) all networks. In the machine learning stage, we used the ensemble classifier Bagging with Random Forest, and 10fold cross-validation repeated 100 times. The experiment using all measures and all spectrograms revealed a satisfactory result, indicating that the MGC proposed is promising. We also propose a new equation to calculate the GLSI code, which proved to be much faster and with more intuitive encoding.

关键词： Digital image processing computer vision applications pattern recognition performance evaluation

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Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation

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APPLIED OPTICS 2021年第19期60卷 5496-5506页

作者： Karangwa, Jules Kong, Linghua Yi, Dingrong Zheng, Jishi Fujian Univ Technol Internet Things Lab Dept Mech Engn Fac Mech Engn Fuzhou 350000 Peoples R China Huaqiao Univ Coll Mech Engn & Automat Xiamen 361021 Peoples R China Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350000 Peoples R China

The tendency to increase the accuracy and quality of optical parts inspection can be observed all over the world. The imperfection of manufacturing techniques can cause different defects on the optical component surface, making surface defects inspection a crucial part of the manufacturing of optical components. Currently, the inspection of lenses, filters, mirrors, and other optical components is performed by human inspectors. However, human-based inspections are time-consuming, subjective, and incompatible with a highly efficient high-quality digital workflow. Moreover, they cannot meet the complex criteria of ISO 10110-7 for the quality pass and fail optical element samples. To meet the high demand for high-quality products, intelligent visual inspection systems are being used in many manufacturing processes. Automated surface imperfection detection based on machine learning has become a fascinating and promising area of research, with a great direct impact on different visual inspection applications. In this paper, an optical inspection platform combining parallel deep learning-based image-processing approaches with a high-resolution optomechanical module was developed to detect surface defects on optical plane components. The system involves the mechanical modules, the illumination and imaging modules, and the machine vision algorithm. Dark-field images were acquired using a 2448 x 2048-pixel line-scanning CMOS camera with 3.45 mu m per-pixel resolution. Convolutional neural networks and semantic segmentation were used for a machine vision algorithm to detect and classify defects on captured images of optical bandpass filters. The experimental results on different bandpass filter samples have shown the best performance compared to traditional methods by reaching an impressive detection speed of 0.07 s per image and an overall detection pixel accuracy of 0.923. (C) 2021 Optical Society of America

关键词： Bandpass filters CMOS cameras Complementary metal oxide semiconductors image processing Optical components Optical elements

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C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using vision Transformer Networks

C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Visi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sairam VC Rebbapragada Pranoy Panda Vineeth N Balasubramanian Machine Learning and Vision Group IIT Hyderabad India Fujitsu AI Research India

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. However, detecting drones presents unique challenges, including small object sizes, distortion, occlusion, and real-time processing requirements. Current methods integrating multi-scale feature fusion and temporal information have limitations in handling extreme blur and minuscule objects. To address this, we propose a novel coarse-to-fine detection strategy based on vision transformers. We evaluate our approach on three challenging drone- to-drone detection datasets, achieving F1 score enhancements of 7%, 3%, and 1% on the FL-Drones, AOT, and NPS-Drones datasets, respectively. Additionally, we demonstrate real-time processing capabilities by deploying our model on an edge-computing device. Our code will be made publicly available.

关键词： Computer vision Codes image edge detection image representation Transformers Distortion Real-time systems

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