160 research outputs found
Novel MobileNet based Multipath Convolutional Neural Network for defect detection in fabrics
Automatic fabric defect detection and classification is the most important process in the textile industry to ensure the fabric quality. In the existing systems, a learning based method is used for detecting defects in plain weave fabrics. In this paper, a novel MobileNet based Multipath Convolutional Neural Network (MMPCNN) architecture is proposed for detection and classification of simple and complex patterned fabric defects. In the proposed MMPCNN architecture, MobileNet model is used in the first path. In this, Gabor filter bank is used instead of conventional filters in the first convolution layer. A simple convolutional neural network architecture with Gray Level Co-occurrence Matrix (GLCM) features as an input is used in the second path of the MMPCNN architecture. Gabor filters are more useful for analyzing the texture with different orientations and scales. Each Gabor filter parameter has its own impact on analyzing the texture and extracting the information from the texture. Therefore, in this paper, the use of Gabor filter parameters in MMPCNN architecture is analyzed. The proposed model is experimented on the TILDA textile image database and it is able to achieve 100% accuracy with reduced trainable parameters for fabric defect detection and classification
Texture and Colour in Image Analysis
Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews
Deep Industrial Image Anomaly Detection: A Survey
The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metrics and datasets. In addition, we extract the new setting
from industrial manufacturing and review the current IAD approaches under our
proposed our new setting. Moreover, we highlight several opening challenges for
image anomaly detection. The merits and downsides of representative network
architectures under varying supervision are discussed. Finally, we summarize
the research findings and point out future research directions. More resources
are available at
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection
Fabric defect segmentation is integral to textile quality control. Despite
this, the scarcity of high-quality annotated data and the diversity of fabric
defects present significant challenges to the application of deep learning in
this field. These factors limit the generalization and segmentation performance
of existing models, impeding their ability to handle the complexity of diverse
fabric types and defects. To overcome these obstacles, this study introduces an
innovative method to infuse specialized knowledge of fabric defects into the
Segment Anything Model (SAM), a large-scale visual model. By introducing and
training a unique set of fabric defect-related parameters, this approach
seamlessly integrates domain-specific knowledge into SAM without the need for
extensive modifications to the pre-existing model parameters. The revamped SAM
model leverages generalized image understanding learned from large-scale
natural image datasets while incorporating fabric defect-specific knowledge,
ensuring its proficiency in fabric defect segmentation tasks. The experimental
results reveal a significant improvement in the model's segmentation
performance, attributable to this novel amalgamation of generic and
fabric-specific knowledge. When benchmarking against popular existing
segmentation models across three datasets, our proposed model demonstrates a
substantial leap in performance. Its impressive results in cross-dataset
comparisons and few-shot learning experiments further demonstrate its potential
for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table
Image Classification of High Variant Objects in Fast Industrial Applications
Recent advances in machine learning and image processing have expanded the applications of computer vision
in many industries. In industrial applications, image classification is a crucial task since high variant objects
present difficult problems because of their variety and constant change in attributes. Computer vision algorithms
can function effectively in complex environments, working alongside human operators to enhance efficiency and
data accuracy. However, there are still many industries facing difficulties with automation that have not yet been
properly solved and put into practice. They have the need for more accurate, convenient, and faster methods.
These solutions drove my interest in combining multiple learning strategies as well as sensors and image formats
to enable the use of computer vision for these applications. The motivation for this work is to answer a number of
research questions that aim to mitigate current problems in hinder their practical application. This work therefore
aims to present solutions that contribute to enabling these solutions. I demonstrate why standard methods cannot
simply be applied to an existing problem. Each method must be customized to the specific application scenario
in order to obtain a working solution.
One example is face recognition where the classification performance is crucial for the system’s ability to
correctly identify individuals. Additional features would allow higher accuracy, robustness, safety, and make
presentation attacks more difficult. The detection of attempted attacks is critical for the acceptance of such
systems and significantly impacts the applicability of biometrics. Another application is tailgating detection
at automated entrance gates. Especially in high security environments it is important to prevent that authorized
persons can take an unauthorized person into the secured area. There is a plethora of technology that seem potentially
suitable but there are several practical factors to consider that increase or decrease applicability depending
which method is used. The third application covered in this thesis is the classification of textiles when they are
not spread out. Finding certain properties on them is complex, as these properties might be inside a fold, or differ
in appearance because of shadows and position.
The first part of this work provides in-depth analysis of the three individual applications, including background
information that is needed to understand the research topic and its proposed solutions. It includes the state of
the art in the area for all researched applications. In the second part of this work, methods are presented to
facilitate or enable the industrial applicability of the presented applications. New image databases are initially
presented for all three application areas. In the case of biometrics, three methods that identify and improve
specific performance parameters are shown. It will be shown how melanin face pigmentation (MFP) features
can be extracted and used for classification in face recognition and PAD applications. In the entrance control
application, the focus is on the sensor information with six methods being presented in detail. This includes the
use of thermal images to detect humans based on their body heat, depth images in form of RGB-D images and
2D image series, as well as data of a floor mounted sensor-grid. For textile defect detection several methods and
a novel classification procedure, in free-fall is presented.
In summary, this work examines computer vision applications for their practical industrial applicability and
presents solutions to mitigate the identified problems. In contrast to previous work, the proposed approaches are
(a) effective in improving classification performance (b) fast in execution and (c) easily integrated into existing
processes and equipment
Human face detection techniques: A comprehensive review and future research directions
Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection
Artificial intelligence for advanced manufacturing quality
100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation
Image Analysis and Machine Learning in Agricultural Research
Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed.
Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability.
With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research.
Advisor: Gary L. Hei
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