Relevant bibliographies by topics / Defects detection and classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Defects detection and classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Defects detection and classification"

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Peng, Peiran, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu, and Weihu Zhou. "Automatic Fabric Defect Detection Method Using PRAN-Net." Applied Sciences 10, no. 23 (November 26, 2020): 8434. http://dx.doi.org/10.3390/app10238434.

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Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.
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Zhao, Weidong, Hancheng Huang, Dan Li, Feng Chen, and Wei Cheng. "Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN." Sensors 20, no. 17 (September 1, 2020): 4939. http://dx.doi.org/10.3390/s20174939.

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To meet the practical needs of detecting various defects on the pointer surface and solve the difficulty of detecting some defects on the pointer surface, this paper proposes a transfer learning and improved Cascade-RCNN deep neural network (TICNET) algorithm for detecting pointer defects. Firstly, the convolutional layers of ResNet-50 are reconstructed by deformable convolution, which enhances the learning of pointer surface defects by feature extraction network. Furthermore, the problems of missing detection caused by internal differences and weak features are effectively solved. Secondly, the idea of online hard example mining (OHEM) is used to improve the Cascade-RCNN detection network, which achieve accurate classification of defects. Finally, based on the fact that common pointer defect dataset and pointer defect dataset established in this paper have the same low-level visual characteristics. The network is pre-trained on the common defect dataset, and weights are transferred to the defect dataset established in this paper, which reduces the training difficulty caused by too few data. The experimental results show that the proposed method achieves a 0.933 detection rate and a 0.873 mean average precision when the threshold of intersection over union is 0.5, and it realizes high precision detection of pointer surface defects.
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Zz, Mi, C. Cong, Y. Cheng, and Zhang Hm. "Study on defects detection technique of precise optical element." E3S Web of Conferences 53 (2018): 01037. http://dx.doi.org/10.1051/e3sconf/20185301037.

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Aiming at the problems of low efficiency of traditional detection methods for surface defects of precision optical element and inconvenient detection for optical elements of different calibers, a adjustable optical element defects detecting device for large laser devices is designed. The key technical points of system composition, detection environment, illumination design and image stitching are expounded. According to the characteristics of surface defects of optical element, such as the difference of contour, gray scale, contrast and ambiguity, a classification method based on FCM is proposed. The experimental results show that the system can realize the automatic detection of surface defects, also it can effectively distinguishes micron-scale defects and has good defect recognition performance. The overall average recognition rate reached to 93.3%.
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Jian, Chuan Xia, Jian Gao, and Xin Chen. "A Review of TFT-LCD Panel Defect Detection Methods." Advanced Materials Research 734-737 (August 2013): 2898–902. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2898.

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TFT-LCD panel defect detection has been one of the difficulties in this field because of fuzzy defect boundary, low contrast between defects and background, and low detection speed. The structure of TFT-LCD panels and classification are introduced. Through the analysis of panel defect features, current detection methods for the TFT-LCD panel defects are reviewed. The key technologies of feature extraction and defect classification are analyzed in the defect image recognition of TFT-LCD panel. Meanwhile the methods of fuzzy boundary defect segmentation, image subtraction and image filtering are also discussed. Finally, the characteristics and advantages of these detection methods are concluded, and several key issues for the TFT-LCD defect detection have been proposed for future development.
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Wu, Bao Hua, Lei Duan, Gui Hua Wang, Hai Yang Wang, and Jing Peng. "Gene Expression Programming Based Classification for Automated Birth Defects Detection." Applied Mechanics and Materials 197 (September 2012): 508–14. http://dx.doi.org/10.4028/www.scientific.net/amm.197.508.

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With the rapid development of digital medicine, improving the diagnostic accuracy for birth defects (BD) by using data mining techniques has been paid more attentions by researchers. In this paper, an automated classification technique based on Gene Expression Programming (GEP) to detect the defect infants, named Birth Defects Detection based on Gene Expression Programming (BDD-GEP) is proposed. The main contributions of this paper include: (1) proposing two contrast inequalities (CIs) for birth defects detection: the defection contrasts to normal and the normal contrasts to defection, (2) designing a new fitness function to mine the normal and defect CIs by GEP, (3) presenting a method to select useful CIs for classification, (4) implementing the BDD-GEP algorithm through combining the proposed CIs with k-Nearest Neighbor algorithm. In order to evaluate the proposed classification method, 11,897 infant samples from national center for birth defects monitoring of China were used, and the method was compared with several existing classification methods. The experimental results show that the overall detection accuracy of BDD-GEP was as high as 87.8%. Specifically, the F-measure of the detect samples was about 70.2%, and the F-measure of the normal samples was about 92.3%.
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Czimmermann, Tamás, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo, and Paolo Dario. "Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY." Sensors 20, no. 5 (March 6, 2020): 1459. http://dx.doi.org/10.3390/s20051459.

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This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.
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Lu, Manhuai, and Chin-Ling Chen. "Detection and Classification of Bearing Surface Defects Based on Machine Vision." Applied Sciences 11, no. 4 (February 18, 2021): 1825. http://dx.doi.org/10.3390/app11041825.

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Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification.
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Jiang, Qingsheng, Dapeng Tan, Yanbiao Li, Shiming Ji, Chaopeng Cai, and Qiming Zheng. "Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning." Applied Sciences 10, no. 1 (December 20, 2019): 87. http://dx.doi.org/10.3390/app10010087.

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Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.
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Liu, Zixi, Zhengliang Hu, Longxiang Wang, Tianshi Zhou, Jintao Chen, Zhenyu Zhu, Hao Sui, Hongna Zhu, and Guangming Li. "Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks." Modern Physics Letters B 35, no. 15 (April 15, 2021): 2150263. http://dx.doi.org/10.1142/s0217984921502638.

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The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.
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Lu, Yuzhen, and Renfu Lu. "siritool: A Matlab Graphical User Interface for Image Analysis in Structured-Illumination Reflectance Imaging for Fruit Defect Detection." Transactions of the ASABE 63, no. 4 (2020): 1037–47. http://dx.doi.org/10.13031/trans.13612.

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HIGHLIGHTSA Matlab GUI, siriTool, was developed for structured-illumination reflectance imaging.siriTool enables image preprocessing, feature extraction, and classification.siriTool was demonstrated for detection of spot defects on pickling cucumbers.Abstract. Structured-illumination reflectance imaging (SIRI) is an emerging imaging modality that provides more useful discriminative features for enhancing detection of defects in fruit and other horticultural and food products. In this study, we developed a Matlab graphical user interface (GUI), siriTool (available at https://codeocean.com/capsule/5699671/tree), to facilitate image analysis in SIRI for fruit defect detection. The GUI enables image preprocessing (i.e., demodulation, object segmentation, and image enhancement), feature extraction and selection, and classification. Demodulation is done using a three-phase or two-phase approach depending on the image data acquired, object segmentation (or background removal) is implemented based on automatic unimodal thresholding, and image enhancement is achieved using fast bi-dimensional empirical decomposition followed by selective image reconstructions. For defect detection, features of different types are extracted from the enhanced images, and feature selection is performed to reduce the feature set. Finally, the full or reduced set of features are then input into different classifiers, e.g., support vector machine (SVM), for image-level classifications. An application example is presented on the detection of yellowish subsurface spot defects in pickling cucumbers. SIRI achieved over 98% classification accuracies based on SVM modeling with the extracted features, which were significantly better than the accuracies obtained under uniform illumination. Keywords: Defect detection, Demodulation, Image enhancement, Machine learning, Matlab, Structured illumination.
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Dissertations / Theses on the topic "Defects detection and classification"

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Allanqawi, Khaled Kh S. Kh. "A framework for the classification and detection of design defects and software quality assurance." Thesis, Kingston University, 2015. http://eprints.kingston.ac.uk/34534/.

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In current software development lifecyeles of heterogeneous environments, the pitfalls businesses have to face are that software defect tracking, measurements and quality assurance do not start early enough in the development process. In fact the cost of fixing a defect in a production environment is much higher than in the initial phases of the Software Development Life Cycle (SDLC) which is particularly true for Service Oriented Architecture (SOA). Thus the aim of this study is to develop a new framework for defect tracking and detection and quality estimation for early stages particularly for the design stage of the SDLC. Part of the objectives of this work is to conceptualize, borrow and customize from known frameworks, such as object-oriented programming to build a solid framework using automated rule based intelligent mechanisms to detect and classify defects in software design of SOA. The framework on design defects and software quality assurance (DESQA) will blend various design defect metrics and quality measurement approaches and will provide measurements for both defect and quality factors. Unlike existing frameworks, mechanisms are incorporated for the conversion of defect metrics into software quality measurements. The framework is evaluated using a research tool supported by sample used to complete the Design Defects Measuring Matrix, and data collection process. In addition, the evaluation using a case study aims to demonstrate the use of the framework on a number of designs and produces an overall picture regarding defects and quality. The implementation part demonstrated how the framework can predict the quality level of the designed software. The results showed a good level of quality estimation can be achieved based on the number of design attributes, the number of quality attributes and the number of SOA Design Defects. Assessment shows that metrics provide guidelines to indicate the progress that a software system has made and the quality of design. Using these guidelines, we can develop more usable and maintainable software systems to fulfil the demand of efficient systems for software applications. Another valuable result coming from this study is that developers are trying to keep backwards compatibility when they introduce new functionality. Sometimes, in the same newly-introduced elements developers perform necessary breaking changes in future versions. In that way they give time to their clients to adapt their systems. This is a very valuable practice for the developers because they have more time to assess the quality of their software before releasing it. Other improvements in this research include investigation of other design attributes and SOA Design Defects which can be computed in extending the tests we performed.
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Nouri, Arash. "Correlation-Based Detection and Classification of Rail Wheel Defects using Air-coupled Ultrasonic Acoustic Emissions." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/78139.

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Defected wheel are one the major reasons endangered state of railroad vehicles safety statue, due to vehicle derailment and worsen the quality of freight and passenger transportation. Therefore, timely defect detection for monitoring and detecting the state of defects is highly critical. This thesis presents a passive non-contact acoustic structural health monitoring approach using ultrasonic acoustic emissions (UAE) to detect certain defects on different structures, as well as, classifying the type of the defect on them. The acoustic emission signals used in this study are in the ultrasonic range (18-120 kHz), which is significantly higher than the majority of the research in this area thus far. For the proposed method, an impulse excitation, such as a hammer strike, is applied to the structure. In addition, ultrasound techniques have higher sensitivity to both surface and subsurface defects, which make the defect detection more accurate. Three structures considered for this study are: 1) a longitudinal beam, 2) a lifting weight, 3) an actual rail-wheel. A longitudinal beam was used at the first step for a better understanding of physics of the ultrasound propagation from the defect, as well, develop a method for extracting the signature response of the defect. Besides, the inherent directionality of the ultrasound microphone increases the signal to noise ratio (SNR) and could be useful in the noisy areas. Next, by considering the ultimate goal of the project, lifting weight was chosen, due to its similarity to the ultimate goal of this project that is a rail-wheel. A detection method and metric were developed by using the lifting weight and two type of synthetic defects were classified on this structure. Also, by using same extracted features, the same types of defects were detected and classified on an actual rail-wheel.
Master of Science
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Ngendangenzwa, Blaise. "Defect detection and classification on painted specular surfaces." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-146063.

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The Volvo Trucks cab plant in Umea is one of the northern Sweden’s largestengineering industries. The plant manufactures only cabs for trucks and is one of the most modern production plants in the world. Despite a highly automated and computerized system among many processes, the paint quality inspection process is still mainly performed manually. A real-time automated and intelligent quality inspection for painted cabs is highly desired to decrease the costs and at the same time to increase both the production efficiency and the product quality. This project is one step forward to the automation of paint quality control. Two different issues were treated during this project, namely defect detection and defect classification. These problems were solved by feeding four statistical approaches such as support vector machine, random forests, k-nearest neighbors and neural networks with extracted histogram of oriented gradients features from the captured images. The results revealed that support vector machine and random forests outperformed their contenders in terms of accuracy to both detect and to classify the defects.
Volvokoncernens hyttfabrik i Umeå är en av Norrlands största verkstadsindustrier.Hyttfabriken tillverkar bara hytter för lastbilar och tillhör en av världens modernaste produktionsanläggningar. Trots ett hög automatiserat och datoriserat system bland många processer så är kvalitetsinspektionen av målade hytter fortfarande utförd manuellt. En smart och automatiserad kvalitetskontroll kan leda till lägre kostnader, högre kvalitet samt högre produktions effektivitet. Den här studien är ett steg framåt mot en automatiserad kvalitetskontroll. Två slagsproblem undersöktes närmare i den här studien nämligen defekt inspektion och defekt klassificering. Dessa problem åtgärdades genom att förse fyra statistiskametoder, support vector machine, random forests, k-nearest neighbors och neuralnetworks, med extraherade HOG egenskaper från tagna bilder. Resultaten visade att support vector machine och random forests presterade bättre än dess konkurrenter i förhållande till förmågan att både inspektera och klassificera defekter.
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Yang, Xuezhi, and 楊學志. "Discriminative fabric defect detection and classification using adaptive wavelet." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29913408.

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Rönnqvist, Johannes, and Johannes Sjölund. "A Deep Learning Approach to Detection and Classification of Small Defects on Painted Surfaces : A Study Made on Volvo GTO, Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160194.

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In this thesis we conclude that convolutional neural networks, together with phase-measuring deflectometry techniques, can be used to create models which can detect and classify defects on painted surfaces very well, even compared to experienced humans. Further, we show which preprocessing measures enhances the performance of the models. We see that standardisation does increase the classification accuracy of the models. We demonstrate that cleaning the data through relabelling and removing faulty images improves classification accuracy and especially the models' ability to distinguish between different types of defects. We show that oversampling might be a feasible method to improve accuracy through increasing and balancing the data set by augmenting existing observations. Lastly, we find that combining many images with different patterns heavily increases the classification accuracy of the models. Our proposed approach is demonstrated to work well in a real-time factory environment. An automated quality control of the painted surfaces of Volvo Truck cabins could give great benefits in cost and quality. The automated quality control could provide data for a root-cause analysis and a quick and efficient alarm system. This could significantly streamline production and at the same time reduce costs and errors in production. Corrections and optimisation of the processes could be made in earlier stages in time and with higher precision than today.
I den här rapporten visar vi att modeller av typen convolutional neural networks, tillsammans med phase-measuring deflektometri, kan hitta och klassificera defekter på målade ytor med hög precision, även jämfört med erfarna operatörer. Vidare visar vi vilka databehandlingsåtgärder som ökar modellernas prestanda. Vi ser att standardisering ökar modellernas klassificeringsförmåga. Vi visar att städning av data genom ommärkning och borttagning av felaktiga bilder förbättrar klassificeringsförmågan och särskilt modellernas förmåga att särskilja mellan olika typer av defekter. Vi visar att översampling kan vara en metod för att förbättra precisionen genom att öka och balansera datamängden genom att förändra och duplicera befintliga observationer. Slutligen finner vi att kombinera flera bilder med olika mönster ökar modellernas klassificeringsförmåga väsentligt. Vårt föreslagna tillvägagångssätt har visat sig fungera bra i realtid inom en produktionsmiljö. En automatiserad kvalitetskontroll av de målade ytorna på Volvos lastbilshytter kan ge stora fördelar med avseende på kostnad och kvalitet. Den automatiska kvalitetskontrollen kan ge data för en rotorsaksanalys och ett snabbt och effektivt alarmsystem. Detta kan väsentligt effektivisera produktionen och samtidigt minska kostnader och fel i produktionen. Korrigeringar och optimering av processerna kan göras i tidigare skeden och med högre precision än idag.
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Carroll, L. Blair. "Investigation into the detection and classification of defect colonies using ACFM technolgy." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0007/MQ42360.pdf.

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Wu, Michael. "Transfer Learning Approach to Powder Bed Fusion Additive Manufacturing Defect Detection." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2324.

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Laser powder bed fusion (LPBF) remains a predominately open-loop additive manufacturing process with minimal in-situ quality and process control. Some machines feature optical monitoring systems but lack automated analytical capabilities for real-time defect detection. Recent advances in machine learning (ML) and convolutional neural networks (CNN) present compelling solutions to analyze images in real-time and to develop in-situ monitoring. Approximately 30,000 selective laser melting (SLM) build images from 31 previous builds are gathered and labeled as either “okay” or “defect”. Then, 14 open-sourced CNN were trained using transfer learning to classify the SLM build images. These models were evaluated by F1 score and down selected to the top 3 models. The top 3 models were then retrained and evaluated using Dietterich’s 5x2 cross-validation and compared with pairwise student t-tests. The pairwise t-test results show no statistically significant difference in performance between VGG- 19, Xception, and InceptionResNet. All models are strong candidates for future development and refinement. Additional work addresses the entire model development process and establishes a foundation for future work. Collaborations with computer science students has produced an image pre-processing program to enhance as-taken SLM images. Other outcomes include initial work to overlay CAD layer images and preliminary hardware integration plan for the SLM machine. The results from this work have demonstrated the potential of an optical layer-wise image defect detection system when paired with a CNN.
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Mahendra, Adhiguna. "Methodology of surface defect detection using machine vision with magnetic particle inspection on tubular material." Thesis, Dijon, 2012. http://www.theses.fr/2012DIJOS051.

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[...]L’inspection des surfaces considérées est basée sur la technique d’Inspection par Particules Magnétiques (Magnetic Particle Inspection (MPI)) qui révèle les défauts de surfaces après les traitements suivants : la surface est enduite d’une solution contenant les particules, puis magnétisées et soumise à un éclairage Ultra-Violet. La technique de contrôle non destructif MPI est une méthode bien connue qui permet de révéler la présence de fissures en surface d’un matériau métallique. Cependant, une fois le défaut révélé par le procédé, ladétection automatique sans intervention de l’opérateur en toujours problématique et à ce jour l'inspection basée sur le procédé MPI des matériaux tubulaires sur les sites de production deVallourec est toujours effectuée sur le jugement d’un opérateur humain. Dans cette thèse, nous proposons une approche par vision artificielle pour détecter automatiquement les défauts à partir des images de la surface de tubes après traitement MPI. Nous avons développé étape par étape une méthodologie de vision artificielle de l'acquisition d'images à la classification.[...] La première étape est la mise au point d’un prototype d'acquisition d’images de la surface des tubes. Une série d’images a tout d’abord été stockée afin de produire une base de données. La version actuelle du logiciel permet soit d’enrichir la base de donnée soit d’effectuer le traitement direct d’une nouvelle image : segmentation et saisie de la géométrie (caractéristiques de courbure) des défauts. Mis à part les caractéristiques géométriques et d’intensité, une analyse multi résolution a été réalisée sur les images pour extraire des caractéristiques texturales. Enfin la classification est effectuée selon deux classes : défauts et de non-défauts. Celle ci est réalisée avec le classificateur des forêts aléatoires (Random Forest) dont les résultats sontcomparés avec les méthodes Support Vector Machine et les arbres de décision.La principale contribution de cette thèse est l'optimisation des paramètres utilisées dans les étapes de segmentations dont ceux des filtres de morphologie mathématique, du filtrage linéaire utilisé et de la classification avec la méthode robuste des plans d’expériences (Taguchi), très utilisée dans le secteur de la fabrication. Cette étape d’optimisation a été complétée par les algorithmes génétiques. Cette méthodologie d’optimisation des paramètres des algorithmes a permis un gain de temps et d’efficacité significatif. La seconde contribution concerne la méthode d’extraction et de sélection des caractéristiques des défauts. Au cours de cette thèse, nous avons travaillé sur deux bases de données d’images correspondant à deux types de tubes : « Tool Joints » et « Tubes Coupling ». Dans chaque cas un tiers des images est utilisé pour l’apprentissage. Nous concluons que le classifieur du type« Random Forest » combiné avec les caractéristiques géométriques et les caractéristiques detexture extraites à partir d’une décomposition en ondelettes donne le meilleur taux declassification pour les défauts sur des pièces de « Tool Joints »(95,5%) (Figure 1). Dans le cas des « coupling tubes », le meilleur taux de classification a été obtenu par les SVM avec l’analyse multirésolution (89.2%) (figure.2) mais l’approche Random Forest donne un bon compromis à 82.4%. En conclusion la principale contrainte industrielle d’obtenir un taux de détection de défaut de 100% est ici approchée mais avec un taux de l’ordre de 90%. Les taux de mauvaises détections (Faux positifs ou Faux Négatifs) peuvent être améliorés, leur origine étant dans l’aspect de l’usinage du tube dans certaines parties, « Hard Bending ».De plus, la méthodologie développée peut être appliquée à l’inspection, par MPI ou non, de différentes lignes de produits métalliques
Industrial surface inspection of tubular material based on Magnetic Particle Inspection (MPI) is a challenging task. Magnetic Particle Inspection is a well known method for Non Destructive Testing with the goal to detect the presence of crack in the tubular surface. Currently Magnetic Particle Inspection for tubular material in Vallourec production site is stillbased on the human inspector judgment. It is time consuming and tedious job. In addition, itis prone to error due to human eye fatigue. In this thesis we propose a machine vision approach in order to detect the defect in the tubular surface MPI images automatically without human supervision with the best detection rate. We focused on crack like defects since they represent the major ones. In order to fulfill the objective, a methodology of machine vision techniques is developed step by step from image acquisition to defect classification. The proposed framework was developed according to industrial constraint and standard hence accuracy, computational speed and simplicity were very important. Based on Magnetic Particle Inspection principles, an acquisition system is developed and optimized, in order to acquire tubular material images for storage or processing. The characteristics of the crack-like defects with respect to its geometric model and curvature characteristics are used as priory knowledge for mathematical morphology and linear filtering. After the segmentation and binarization of the image, vast amount of defect candidates exist. Aside from geometrical and intensity features, Multi resolution Analysis wasperformed on the images to extract textural features. Finally classification is performed with Random Forest classifier due to its robustness and speed and compared with other classifiers such as with Support Vector Machine Classifier. The parameters for mathematical morphology, linear filtering and classification are analyzed and optimized with Design Of Experiments based on Taguchi approach and Genetic Algorithm. The most significant parameters obtained may be analyzed and tuned further. Experiments are performed ontubular materials and evaluated by its accuracy and robustness by comparing ground truth and processed images. This methodology can be replicated for different surface inspection application especially related with surface crack detection
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Janošík, Zdeněk. "Klasifikace detekovaných vad." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221300.

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In this master thesis is described how to design and implement classifier of defects detected during the final stage of production nonwovens. The beginning of the thesis is devoted to the analysis of options for image processing and classification. Followed by the part, where is described process of image segmentation and extraction of feature vector. Description of classifier implementation and table of achieved results of classification on real images of detected defects.
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Mahmood, Waqas, and Muhammad Faheem Akhtar. "Validation of Machine Learning and Visualization based Static Code Analysis Technique." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4347.

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Software security has always been an afterthought in software development which results into insecure software. Companies rely on penetration testing for detecting security vulnerabilities in their software. However, incorporating security at early stage of development reduces cost and overhead. Static code analysis can be applied at implementation phase of software development life cycle. Applying machine learning and visualization for static code analysis is a novel idea. Technique can learn patterns by normalized compression distance NCD and classify source code into correct or faulty usage on the basis of training instances. Visualization also helps to classify code fragments according to their associated colors. A prototype was developed to implement this technique called Code Distance Visualizer CDV. In order test the efficiency of this technique empirical validation is required. In this research we conduct series of experiments to test its efficiency. We use real life open source software as our test subjects. We also collected bugs from their corresponding bug reporting repositories as well as faulty and correct version of source code. We train CDV by marking correct and faulty version of code fragments. On the basis of these trainings CDV classifies other code fragments as correct or faulty. We measured its fault detection ratio, false negative and false positive ratio. The outcome shows that this technique is efficient in defect detection and has low number of false alarms.
Software trygghet har alltid varit en i efterhand inom mjukvaruutveckling som leder till osäker mjukvara. Företagen är beroende av penetrationstester för att upptäcka säkerhetsproblem i deras programvara. Att införliva säkerheten vid tidigt utvecklingsskede minskar kostnaderna och overhead. Statisk kod analys kan tillämpas vid genomförandet av mjukvaruutveckling livscykel. Tillämpa maskininlärning och visualisering för statisk kod är en ny idé. Teknik kan lära mönster av normaliserade kompressionständning avstånd NCD och klassificera källkoden till rätta eller felaktig användning på grundval av utbildning fall. Visualisering bidrar också till att klassificera code fragment utifrån deras associerade färger. En prototyp har utvecklats för att genomföra denna teknik som kallas Code Avstånd VISUALISERARE CDV. För att testa effektiviteten hos denna teknik empirisk validering krävs. I denna forskning vi bedriver serie experiment för att testa dess effektivitet. Vi använder verkliga livet öppen källkod som vår test ämnen. Vi har också samlats in fel från deras motsvarande felrapportering förråd samt fel och rätt version av källkoden. Vi utbildar CDV genom att markera rätt och fel version av koden fragment. På grundval av dessa träningar CDV klassificerar andra nummer fragment som korrekta eller felaktiga. Vi mätt sina fel upptäckt förhållandet falska negativa och falska positiva förhållandet. Resultatet visar att den här tekniken är effektiv i fel upptäckt och har låga antalet falsklarm.
waqasmah@gmail.com +46762316108
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Books on the topic "Defects detection and classification"

1

W, Currie Brian, and Haykin S. S. 1931-, eds. Detection and classification of ice. Letchworth: Research Studies, 1987.

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W, Currie Brian, and Haykin Simon S. 1931-, eds. Detection and classification of ice. Letchworth, Hertfordshire, England: Research Studies Press, 1987.

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Simon, Léa M. Fault detection: Theory, methods and systems. New York: Nova Science Publishers, 2011.

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Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Hoboken, NJ: Wiley, 2008.

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Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Hoboken, NJ: Wiley, 2008.

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Milioris, Dimitrios. Topic Detection and Classification in Social Networks. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-66414-9.

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Gini, Fulvio, and Muralidhar Rangaswamy, eds. Knowledge-Based Radar Detection, Tracking, and Classification. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470283158.

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Janssen, Thomas J. Explosive materials: Classification, composition, and properties. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Goldstein, Robert V., and Gerard A. Maugin, eds. Surface Waves in Anisotropic and Laminated Bodies and Defects Detection. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-2387-1.

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Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.

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Book chapters on the topic "Defects detection and classification"

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Krautkrämer, Josef, and Herbert Krautkrämer. "Detection and Classification of Defects." In Ultrasonic Testing of Materials, 312–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-10680-8_20.

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Mahouachi, Rim, Marouane Kessentini, and Khaled Ghedira. "A New Design Defects Classification: Marrying Detection and Correction." In Fundamental Approaches to Software Engineering, 455–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28872-2_31.

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Louban, Roman. "Adaptive Edge and Defect Detection as a basis for Automated Lumber Classification and Optimisation." In Image Processing of Edge and Surface Defects, 99–128. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00683-8_7.

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Christodoulou, Symeon E., Georgios M. Hadjidemetriou, and Charalambos Kyriakou. "Pavement Defects Detection and Classification Using Smartphone-Based Vibration and Video Signals." In Advanced Computing Strategies for Engineering, 125–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91635-4_7.

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Ochoa-Zezatti, Alberto, Oliverio Cruz-Mejía, Jose Mejia, and Hazael Ceron-Monroy. "Image Classification Applied to the Detection of Leather Defects for Smart Manufacturing." In Computer Science and Health Engineering in Health Services, 52–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69839-3_4.

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Monno, Shota, Yoshifumi Kamada, Hiroyoshi Miwa, Koji Ashida, and Tadaaki Kaneko. "Detection of Defects on SiC Substrate by SEM and Classification Using Deep Learning." In Advances in Intelligent Networking and Collaborative Systems, 47–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98557-2_5.

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Yu, Zhibin, Qiqin Liao, and Shuangmao Yang. "Detection and Classification of Transmission Line Insulator Defects with Res-SSD Optimization Method." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 333–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_39.

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Gebhardt, Wolfgang, and Friedhelm Walte. "Crack Detection and Defect Classification Using the LLT-Technique." In Review of Progress in Quantitative Nondestructive Evaluation, 591–98. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0817-1_74.

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Lopez, Jose J., Emanuel Aguilera, and Maximo Cobos. "Defect Detection and Classification in Citrus Using Computer Vision." In Neural Information Processing, 11–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_2.

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Dudek, Grzegorz, and Sebastian Dudzik. "Classification Tree for Material Defect Detection Using Active Thermography." In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, 118–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67220-5_11.

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Conference papers on the topic "Defects detection and classification"

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Mu, Hongbo, Li Li, Lei Yu, Mingming Zhang, and Dawei Qi. "Detection and Classification of Wood Defects by ANN." In 2006 International Conference on Mechatronics and Automation. IEEE, 2006. http://dx.doi.org/10.1109/icma.2006.257659.

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Silva, Jeronimo A., Cassiano P. Pais, Jose C. A. Freitas, Fernando D. Carvalho, and Fernando C. Rodrigues. "Detection and automatic classification of defects in ceramic products." In International Conference on Manufacturing Automation, edited by Anand K. Asundi and S. T. Tan. SPIE, 1993. http://dx.doi.org/10.1117/12.138502.

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Adachi, Kazune, Takahiro Natori, and Naoyuki Aikawa. "Detection and classification of painting defects using deep learning." In 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2021. http://dx.doi.org/10.1109/itc-cscc52171.2021.9524736.

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LUO, Changhao, Kai ZHAO, Xiaoxia Sun, Min MIAO, and Zhensong LI. "Detection and Classification of Typical Defects in TSV and RDL." In 2019 20th International Conference on Electronic Packaging Technology(ICEPT). IEEE, 2019. http://dx.doi.org/10.1109/icept47577.2019.245135.

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Jiang, Jiabin, Xiang Xiao, Guohua Feng, Zichen Lu, and Yongying Yang. "Detection and classification of glass defects based on machine vision." In Applied Optical Metrology III, edited by Erik Novak and James D. Trolinger. SPIE, 2019. http://dx.doi.org/10.1117/12.2528654.

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Liu, Luye, Bahador Bahramimianrood, Han Zou, and Suixian Yang. "Welding Defects Detection and Classification by Using Eddy Current Thermography." In 2017 Far East NDT New Technology & Application Forum (FENDT). IEEE, 2017. http://dx.doi.org/10.1109/fendt.2017.8584572.

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Dat, Duong Tan, Nguyen Dao Xuan Hai, and Nguyen Truong Thinh. "Detection and Classification Defects on Exported Banana Leaves by Computer Vision." In 2019 International Conference on System Science and Engineering (ICSSE). IEEE, 2019. http://dx.doi.org/10.1109/icsse.2019.8823097.

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Hittawe, Mohamad Mazen, Satya M. Muddamsetty, Desire Sidibe, and Fabrice Meriaudeau. "Multiple features extraction for timber defects detection and classification using SVM." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7350834.

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Pastor-Lopez, Iker, Igor Santos, Jorge de-la-Pena-Sordo, Mikel Salazar, Aitor Santamaria-Ibirika, and Pablo G. Bringas. "Collective classification for the detection of surface defects in automotive castings." In 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA 2013). IEEE, 2013. http://dx.doi.org/10.1109/iciea.2013.6566502.

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Chen, Aris, Victor Huang, Sophie Chen, C. J. Tsai, Kenneth Wu, Haiping Zhang, Kevin Sun, et al. "Advanced inspection methodologies for detection and classification of killer substrate defects." In SPIE Lithography Asia - Taiwan, edited by Alek C. Chen, Burn Lin, and Anthony Yen. SPIE, 2008. http://dx.doi.org/10.1117/12.804558.

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Reports on the topic "Defects detection and classification"

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Cheadle, Nancy, Dennis Tackett, Robert Pierce, and Raymond de Lacaze. Automatic Detection of Radar Signature Defects,. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada364069.

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Dr. Gabe V. Garcia. Automated Diagnosis and Classification of Steam Generator Tube Defects. Office of Scientific and Technical Information (OSTI), October 2004. http://dx.doi.org/10.2172/833464.

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Houston, Brian H., Tim Yoder, and Larry Carin. Harbor Threat Detection, Classification, and Identification. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612415.

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Houston, Brian H., and Larry Carin. Harbor Threat Detection, Classification, and Identification. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada541163.

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Bower, James M., Linda Buck, William Goddard, Wilson III, Lewis Denise, and Nathan S. Understanding Olfaction: From Detection to Classification. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada428676.

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Mellinger, David K. Detection, Classification, and Localization Workshop 2011. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada598568.

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Lohrenz, M. C., M. L. Gendron, and G. J. Layne. Automic Change Detection and Classification (ACDC) System. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada494240.

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Author, Not Given. A sequential detection algorithm for color printed pattern defects. Office of Scientific and Technical Information (OSTI), September 1995. http://dx.doi.org/10.2172/10129734.

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Birch, Gabriel Carisle, John Clark Griffin, and Matthew Kelly Erdman. UAS Detection Classification and Neutralization: Market Survey 2015. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1222445.

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Mellinger, David K. Detection, Classification, and Density Estimation of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada579344.

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