CPU, GPU i FPGA implementacija MALD algoritma za otkrivanje nepravilnosti na površini keramičkih pločica

This paper addresses adjustments, implementation and performance comparison of the Moving Average with Local Difference (MALD) method for ceramic tile surface defects detection. Ceramic tile production process is completely autonomous, except the final stage where human eye is required for defects detection. Recent computational platform development and advances in machine vision provides us with several options for MALD algorithm implementation. In order to exploit the shortest execution time for ceramic tile production process, the MALD method is implemented on three different platforms: CPU, GPU and FPGA, and it is implemented on each platform in at least two ways. Implementations are done in MATLAB’s MEX/C++, C++, CUDA/C++, VHDL and Assembly programming languages. Execution times are measured and compared for different algorithms and their implementations on different computational platforms.U ovom radu razmatra se prilagodba, implementacija i usporedba performansi metode pomičnog usrednjavanja s lokalnom diferencijom (MALD) s primjenom u otkrivanju površinskih nedostataka na keramičkim pločicama. Proizvodna linija keramičkih pločica je autonomna sve do zadnje faze u kojoj je potreban ljudski vid kako bi se otkrili eventualni nedostaci na keramičkim pločicama. Nedavnim razvojem računalnih platformi i razvojem metoda računalnog vida omogućena je implementacija MALD metode na nekoliko načina. U nastojanju skraćenja vremena potrebnog za proizvodnju keramičkih pločica, MALD metoda je implementirana u trima različitim platformama: CPU (central processing unit), GPU (graphic processing unit) i FPGA (field programmable gate array), te s barem dva različita algoritma. Implementacija je izvršena sa MATLAB MEX/C++, C++, CUDA/C++, VHDL te Asembler programskim jezicima. Izmjerena vremena obrade su me.usobno uspore.ena za različite algoritme i njihove implementacije na različitim računalnim platformama

The hybrid design of supervised learning algorithm for design and development in classifications a defect in clay tiles

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards.  One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight.  It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods.  Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify

Deteksi Cacat Ubin Keramik Dengan Metode K-Nearest Neighbor

Perusahaan industri manufaktur harus dapat menjaga kualitas dari setiap produk yang diproduksi, termasuk perusahaan industri manufaktur yang memproduksi ubin keramik. Selama beberapa tahun, inspeksi visual secara otomatis sudah diterapkan untuk menentukan kualitas ubin keramik yang diproduksi. Sulitnya mendeteksi ubin keramik yang cacat bisa berdampak pada menurunnya kualitas hasil produksi, menurunnya tingkat kepercayaan konsumen, dan penurunan laba bagi perusahaan. Masalah yang dibahas di dalam penelitian ini adalah bagaimana mendeteksi ubin keramik yang cacat sehingga model yang dibangun dapat meningkatkan akurasi untuk mendeteksi ubin keramik yang cacat. Langkah penyelesaian masalah ini adalah dengan mengumpulkan data berupa citra dari ubin keramik, kemudian data citra dilakukan preprocessing menggunakan Median Filtering untuk menghilangkan noise salt and paper dan Teknik Morfologi untuk memperbaiki hasil segmentasi citra. Setelah dilakukan preprocessing, data citra diekstraksi ciri berdasarkan tekstur dengan menggunakan metode Gray Level Co-occurrence Matrix (GLCM) yang dilanjutkan dengan mengklasifikasikan data citra menggunakan metode K-Nearest Neighbor (KNN). Hasil dari penelitian ini adalah model yang dibangun menggunakan metode K-Nearest Neighbor dapat meningkatkan akurasi untuk mendeteksi kecacatan pada ubin keramik dengan nilai akurasi sebesar 98.9474% untuk k = 3

Perbandingan Cacat Ubin Keramik dengan Metode K-Nearest Neighbor dan Support Vector Machine

Penentuan kualitas ubin keramik sudah dilakukan secara otomatis dalam beberapa tahun terakhir. Kendala saat penentuan ubin keramik bercacat dapat berpengaruh terhadap penurunan kualitas produk akhir. Isu yang menjadi fokus dalam penelitian yaitu perbandingan metode antara KNN dengan SVM untuk mendeteksi cacat pada ubin keramik untuk mencapai hasil yang lebih akurat. Untuk mengatasi isu ini, proses yang dilakukan meliputi pengumpulan data gambar dari ubin keramik, yang kemudian diikuti oleh tahap preprocessing dan ekstraksi fitur berdasarkan tekstur. Data gambar tersebut kemudian diklasifikasikan dengan metode KNN dan SVM. Temuan dari penelitian ini menunjukkan bahwa pengklasifikasian dengan metode KNN pada k = 3 mampu memberikan hasil yang lebih unggul, yaitu mencapai akurasi 98.947%, sedangkan pengklasifikasian dengan metode SVM hanya mencapai akurasi 85.263%

The Performance Analysis of the Thermal Discrete Element Method Computations on the GPU

The paper presents a GPU implementation of the thermal discrete element method (TDEM) and the comparative analysis of its performance. Several discrete element models for granular flows, the bonded particle model and the TDEM are considered for quantitative comparison of computational performance. The performance measured on NVIDIA(R) Tesla™ P100 GPU is compared with that attained by running the same OpenCL code on Intel(R) Xeon™ E5-2630 CPU with 20 cores. The presented GPU implementation of the TDEM increases the computing time of the bonded particle model only up to 30.6 % of the computing time of the simplest DEM model, which is an acceptable decrease in the performance required for solving coupled thermomechanical problems

Deteção de veículos e edifícios em imagens aéreas obtidas por drone

The need to develop software for aerial image analysis, captured by Unmanned Aerial Vehicles, has increased over the years because their use has become more prevalent in different day-to-day scenarios. Object detection, a Computer Vision technique, is one of the most explored problems in this area and consists of identifying and locating objects in images or videos, with the help of Artificial Intelligence technologies. The aim of this dissertation is to analyze the performance of Deep Learning algorithms for detecting vehicles and buildings in aerial images. Two of the main algorithms described in literature, Faster R-CNN and YOLO, the latter in the third and fifth versions, were chosen to verify which one is capable of better performance. The dataset provided by the Portuguese Military Academy, which was annotated and pre-processed, was used for the training of each algorithm and the performance of tests. The results obtained in the abovementioned dataset demonstrate that there is a considerable discrepancy between the two algorithms, both in terms of performance and speed. Faster R-CNN only proved to be superior to the two versions of YOLO in terms of training speed, as it was the algorithm that required less time for training. Among the versions of YOLO, the fifth version showed the best results.A necessidade de desenvolver software para a análise de imagem aérea, capturada por Veículos Aéreos Não Tripulados, tem vindo a aumentar ao longo dos anos devido ao facto de serem cada vez mais utilizadas em diversos cenários do dia-a-dia. A deteção de objetos, técnica da Visão Computacional, é um dos problemas mais explorados nesta área e consiste na identificação e localização de objetos em imagens ou vídeos, com o auxílio de tecnologias de Inteligência Artificial. Pretende-se com esta dissertação analisar o desempenho de algoritmos de Aprendizagem Profunda, para a deteção de veículos e edifícios em imagens aéreas. Foram escolhidos dois dos principais algoritmos descritos na literatura, Faster R-CNN e YOLO, este último na terceira e quinta versão, por forma a verificar qual apresenta melhor desempenho. Para o treino de cada algoritmo e realização de testes foi utilizado um conjunto de dados fornecido pela Academia Militar Portuguesa, o qual foi anotado e pré-processado. Os resultados obtidos, no referido conjunto de dados, demonstraram que existe uma discrepância considerável entre os dois algoritmos, tanto a nível do desempenho como do tempo de deteção. O Faster R-CNN apenas se mostrou superior em relação às duas versões do YOLO no tempo de treino, pois foi o algoritmo que precisou de menos tempo. Entre as versões do YOLO, a quinta versão foi a que apresentou melhores resultados.Mestrado em Engenharia de Computadores e Telemátic

Estudo comparativo de técnicas de análise de textura em imagens e aprendizagem de máquina para classificação de Phragmites australis usando imagens de alta resolução com cor no espectro do visível

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.Phragmites australis (common reed) comumente encontrada em zonas úmidas costeiras pode alterar rapidamente a ecologia por competir e superar as plantas nativas por espaço e pelos recursos. Além disso, este tipo de vegetação representa um perigo de navegação para embarcações menores, prejudicando a visibilidade ao longo do litoral e em torno de curvas e canais de rios. Os esforços de gerencialmento direcionados a plantas não nativas de Phragmites dependem fortemente de um mapeamento preciso das áreas invadidads. No entanto, o mapeamento de Phragmites representa um desafio único por diferentes razões. Identificar e mapear Phragmites pode ajudar os gerentes de recurso a restaurar zonas húmidas afetadas. Neste trabalho, quatro técnicas de extração de características foram testadas: gabor filters, grey level co-occurrence matrix, segmentation-based fractal texture analysis e wavelet texture analysis. Estes algoritmos foram combinados com três estruturas de rede neural artificial: multilayer perceptron, probabilistic neural network e radial basis function network. Além disso, objetivando reduzir o tempo computacional, uma implementação na Graphics Processing Unit do melhor método identificado foi realizada. O estudo de avaliação foi realizado com imagens adquiridas no delta de Pearl River localizado no sudeste da Louisiana e no sudoeste do Mississippi, Estados Unidos da América. Em comparação com os resultados apresentados no estado da arte, wavelet texture analysis com probabilistic neural network e segmentation-based fractal texture analysis com probabilistic neural network apresentaram melhorias em várias variáveis estatísticas como acurácia geral e o kappa. Além disso, o nível de Phragmites agreement aumentou considerávelmente. Nos mostramos que os erros de omissão e comissão restantes geralmente estão localizados ao longo dos limites das áreas identificadas como Phragmites, o que reduz os esforços desnecessários para os gerentes de recursos na busca de áreas inexistentes.Phragmites australis (common reed) commonly found in the coastal wetlands can rapidly alter the ecology by outcompeting with natives for space and resources. In addition, this type of vegetation presents a navigation hazard to smaller boats by impairing visibility along shorelines and around bends of canals and rivers. Management efforts targeting non-native Phragmites rely heavily on accurately mapping invaded areas. However, mapping Phragmites represents a unique challenge for different reasons. Identifying and mapping Phragmites can help resource managers to restore affected wetlands. In this work, four feature extraction methods were tested: gabor filters, grey level co-occurrence matrix, segmentation-based fractal texture analysis, and wavelet texture analysis. These algorithms were combined with three artificial neural network architectures: multilayer perceptron, probabilistic neural network, and radial basis function network. In addition, aiming to reduce the computational cost, a graphics processing unit implementation of the best result was performed. Evaluation study was conducted with imagery acquired in the delta of Pearl River located in southeastern Louisiana and southwestern Mississippi, United States of America. In comparison to state-of-art results, wavelet texture analysis with probabilistic neural network and segmentation-based fractal texture analysis with probabilistic neural network presented presented improvements in several statistical variables such as overall accuracy and kappa value. Furthermore, the Phragmites agreement increased considerably. We show that the remaining omission and commission errors are generally located along boundaries of patches with Phragmites, which reduces unnecessary efforts for resource managers while searching for nonexistent patches