Camera geometry determination based on circular's shape for peg-in-hole task

A simple, inexpensive system and effective in performing required tasks is the most preferable in industry. The peg-in-hole task is widely used in manufacturing process by using vision system and sensors. However, it requires complex algorithm and high Degree of Freedom (DOF) mechanism with fine movement. Hence, it will increase the cost. Currently, a forklift-like robot controlled by an operator using wired controllers is used to pick up one by one of the copper wire spools arranged side by side on the shelf to be taken to the inspection area. The holder and puller attached to the robot is used to pick up the spool. It is difficult for the operator to ensure the stem is properly inserted into the hole (peg-in-hole problem) because of the structure of the robot. However, the holder design is not universal and not applicable to other companies. The spool can only be grasped and pulled out from the front side and cannot be grasped using robot arm and gripper. In this study, a vision system is developed to solve the peg-in-hole problem by enabling the robot to autonomously perform the insertion and pick up the spool without using any sensors except a low-cost camera. A low-cost camera is used to capture images of copper wire spool in real-time video. Inspired by how human perceive an object orientation based on its shape, a system is developed to determine camera orientation based on the spool image condition and yaw angle from the center of the camera (CFOV) to CHS. The performance of the proposed system is analyzed based on detection rate analysis. This project is developed by using MATLAB software. The analysis is done in controlled environment with 50-110 cm distance range of camera to the spool. In addition, the camera orientation is analyzed between -20º to 20º yaw angle range. In order to ensure the puller will not scratch the spool, a mathematical equation is derived to calculate the puller tolerance. By using this, the system can estimate the spool position based on the camera orientation and distance calculation. Application of this system is simple and costeffective. A Modified Circular Hough Transform (MCHT) method is proposed and tested with existing method which is Circular Hough Transform (CHT) method to eliminate false circles and outliers. The results of the analysis showed detection success rate of 96% compared to the CHT method. It shows the MCHT method is better than CHT method. The proposed system is able to calculate the distance and camera orientation based on spool image condition with low error rate. Hence, it solves the peg-in-hole problem without using Force/Torque sensor. In conclusion, a total of 7 analysis consist of image pre-processing, image segmentation, object classification, comparison between CHT and MCHT, illumination measurement, distance calculation and yaw angle analysis were experimentally tested including the comparison with the existing method. The proposed system was able to achieve all the objectives

Protótipo de dispositivo automatizado para leitura do teste de antibiograma por disco-difusão

O antibiograma por disco-difusão é uma das técnicas mais utilizadas para determinar o perfil de suscetibilidade antimicrobiana. Consiste na difusão de diferentes agentes antimicrobianos na superfície de uma placa de Petri contendo a semeadura de uma bactéria, formando-se halos de inibição com diâmetros proporcionais a eficá- cia de cada agente. Devido ao rápido aumento da resistência bacteriana em âmbito mundial, é importante a rápida e correta identificação da bactéria e seu perfil nos laboratórios de microbiologia de clínicas e hospitais. Em laboratório sem recursos tecnológicos, estas medidas são tomadas manualmente utilizando réguas ou paquí- metros. É um método lento e tedioso, além de aumentar a chance de erros. Este trabalho apresenta o desenvolvimento de um protótipo de um sistema automatizado para medição das zonas de inibição formadas nos testes de antibiograma por disco-difusão. Este sistema consiste em um aparato para aquisição das imagens das placas de Petri e um software para estimação dos diâmetros dos halos formados. O algoritmo proposto pré-processa a imagem, realçando seu contraste através da equalização por histograma e suavizando por meio de um filtro gaussiano. Mediante o uso da transformada de Hough circular, a posição central de cada halo de inibição é encontrada e por meio de um algoritmo de análise de perfil radial e clusterização, os diâmetros são estimados. Imagens de 49 placas, com uma média de 11 zonas de inibição cada, foram coletadas e o resultado encontrado pelo sistema com o algoritmo foi comparado aos obtidos com o equipamento comercial de referência Osiris® (este com com correção manual das medidas pelo operador). O algoritmo proposto identificou corretamente as posições nas imagens de todos os 564 halos analisados. O coeficiente de correlação de Pearson entre as medidas de ambos os sistemas foi de 0,8893. Já o coeficiente Kappa de Cohen foi de 0,7799 e a concordância total foi de 89,68%. Este estudo foi aprovado pelo Comitê de Ética em Pesquisa do HCPA sob o número 170587.The disk diffusion method is still one of the most frequently used method to determine the antibiotic susceptibility profiles of pathogens. It consists of the diffusion of different antimicrobial agents on the surface of a Petri dish containing the seeding of a bacterium, forming inhibition halos with diameters proportional to the effectiveness of each agent. Due to the rapid rise in antimicrobial resistance worldwide it is important the rapid identification of their resistance profile in the microbiology laboratory routine. In laboratories without technological facilities such measurement is done manually using a ruler or a caliper. This method is time consuming and increases the chances of error. The aim of this study was the development of a system for the measurement of the growth inhibition zone diameter in the antibiogram disk diffusion method. The system consists of an apparatus for plate images acquisition and a software for estimation of the inhibition zone diameters. The proposed algorithm preprocesses the acquired image and improves its contrast by means of histogram equalizing and smoothing by a Gaussian filter. Using the Hough transform method, the central position of each inhibition zone is identified and through a radial profile analysis and clustering algorithm, the diameters are estimated. Images of 49 plates containing an average of 11 inhibition zones were acquired and the result of the algorithm was compared with the results obtained by the commercial equipment Osiris® (this with correction of the measurements by the operator). The proposed algorithm correctly identified the position of the 564 inhibition zones in 100% of the images. The Pearson correlation coeffcient between the measurements of the inhibition zones diameters by the proposed algorithm and Osiris® was 0.8893. The Cohen’s kappa coeffcient between Osiris® and the proposed method was 0.7799 and the concordance between methods was 89.68%

Sistema de posicionamento robotizado segundo o conceito da indúsria 4.0

Trabalho final de mestrado para obtenção do grau de mestre em Engenharia MecânicaCom a evolução da automação industrial, existe a necessidade de automatizar até os processos mais simples, de forma a manter as empresas industriais competitivas. O objectivo deste Trabalho Final de Mestrado é automatizar o processo de posicionamento do sistema de lavagem interior dos tanques dos camiões-cisterna, com supervisão a partir da Internet. Neste trabalho de projecto desenvolveu-se um sistema de posicionamento robotizado com controlo de posição através da imagem de câmaras de vídeo. O algoritmo de visão foi programado no software Matlab com auxílio de bibliotecas de visão computacional e de comunicação com um controlador de motores de passo desenvolvido em arquitectura de Arduino. O desenvolvimento foi feito dentro do conceito da indústria 4.0, ou seja, todo o sistema é totalmente automático com excepção da introdução do número de bocas pelo operador. Foram testados controladores com comunicação à base do protocolo Ethernet, nomeadamente o PC com o cliente do Matlab a comunicar com um servidor Apache para que seja possível estabelecer a conexão entre o sistema e a supervisão na Internet. Todo o sistema pode ser supervisionado através de um smartphone ou tablet a partir do browser instalado. Construiu-se também um protótipo à escala, seleccionado de modo a validar o algoritmo de visão e as simulações efectuadas em Matlab. O protótipo é constituído por uma estrutura em perfis V-slot, motores de passo, o controlador e câmara USB, que embora tenha uma menor qualidade de imagem, foi suficiente para validar o algoritmo de visão implementado.With the evolution of industrial automation, there is a need to automate even the simplest processes, to keep industrial companies competitive. The objective of this Master’s Thesis is to automate the positioning system of an internal washing process of tanker trucks, with supervision from the Web. In this project work, a robot positioning system was developed with position control through the image of video cameras. The vision algorithm was programmed in Matlab software with the help of computer vision toolboxes and communication with a stepper motor controller developed in Arduino architecture. The development was done within the concept of the industry 4.0, that is, the whole system is totally automatic except for the introduction of the number of holes by the operator. Controllers were tested with communication based on the Ethernet protocol, namely the PC with the Matlab client to communicate with an Apache server so that it is possible to establish the connection between the system and the supervision on the web. The entire system can be supervised through a smartphone or tablet from the installed browser. A prototype was also built to scale, selected to validate the vision algorithm and the simulations performed in Matlab. The prototype consists of a structure in V-slot profiles, as well as the controller and USB camera that although having a lower quality of image, was enough to validate the implemented vision algorithm.N/

Metalik yansımalı yüzeylerde otomatik çizik tespiti için görüntü işleme sistemi.

In industry, problems due to human error, mechanical flaws and transportation may occur; besides, they need to be detected in fast and efficient ways. In order to eliminate failure of human inspection, automated systems come in action, usually image processing involved. This thesis work, targets one common mass production problem on specular surfaces, i.e. scratch detection. To achieve this, we have implemented two different prototypes. The low-cost system is based on basic line detection, and the mid-end system depends on learning based detection. Both systems are implemented on embedded platforms and performance comparisons are done. Detailed analysis is carried out on computational cost and detection performance. This real-world episode is done on a mechanical prototype in laboratory environmentM.S. - Master of Scienc

Detection of counterfeit coins based on 3D Height-Map Image Analysis

Analyzing 3-D height-map images leads to the discovery of a new set of features that cannot be extracted or even seen in 2-D images. To the best of our knowledge, there was no research in the literature analyzing height-map images to detect counterfeit coins or to classify coins. The main goal of this thesis is to propose a new comprehensive method for analyzing 3D height-map images to detect counterfeit of any type of coins regardless of their country of origin, language, shape, and quality. Therefore, we applied a precise 3-D scanner to produce coin height-map images, since detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. In this research, we propose some 3-D approaches to model and analyze several large datasets. In our first and second methods, we aimed to solve the degradation problem of shiny coin images due to the scanning process. To solve this problem, first, the characters of the coin images were straightened by a proposed straightening algorithm. The height-map image, then, was decomposed row-wise to a set of 1-D signals, which were analyzed separately and restored by two different proposed methods. These approaches produced remarkable results. We also proposed a 3-D approach to detect and analyze the precipice borders from the coin surface and extract significant features that ignored the degradation problem. To extract the features, we also proposed Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. We also took advantage of stack generalization to classify the coins and add a reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors. Since there are traces of deep learning in most recent research related to image processing, it is worthwhile to benefit from deep learning approaches in our study. In another proposed method of this thesis, we applied deep learning algorithms in two steps to detect counterfeit coins. As Generative Adversarial Network is being used for generating fake images in image processing applications, we proposed a novel method based on this network to augment our fake coin class and compensate for the lack of fake coins for training the classifier. We also decomposed the coin height-map image into three types of Steep, Moderate, and Gentle slopes. Therefore, the grayscale height-map image is turned to the proposed SMG height-map channel. Then, we proposed a hybrid CNN-based deep neural network to train and classify these new SMG images. The results illustrated that a deep neural network trained with the proposed SMG images outperforms the system trained by the grayscale images. In this research, the proposed methods were trained and tested with four types of Danish and two types of Chinese coins with encouraging results