Uneven illumination surface defects inspection based on convolutional neural network

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination

Automatic Color Inspection for Colored Wires in Electric Cables

In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions

Thermographic non-destructive evaluation for natural fiber-reinforced composite laminates

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was give

Investigation of the effects of NaOH dopant level on the physical and mechanical properties of carbon/phenolic composite material

The near-catastrophic erosion of the STS-8A solid rocket booster nozzle was the instigating factor in the recent, and on-going, intensive investigation of carbon/phenolic composite materials. Much of this effort has been focused on the effect of sodium contamination on the carbon fibers. It is known that sodium acts as a catalyst in the oxidation of the fibers at elevated temperatures. A study was undertaken to determine what changes were caused by variation in sodium content. Investigations were conducted in three areas: (1) phenolic resin; (2) carbon/phenolic prepreg; and (3) cured laminates. Due to the exploratory nature of the studies, a variety of tests in addition to those normally run in production facilities were considered. The experimental methodologies and results of these experiments are discussed, and recommendations for improving techniques and extending the research program are presented

OPTIMIZING WOVEN FABRIC DEFECT DETECTION FOR INSPECTION USING IMAGE PROCESSING AND FUZZY LOGIC AT CV. MAEMUNAH MAJALAYA

West Java has the largest number of textile industry in Indonesia. One of the companies that engaged in textiles is CV. Maemunah which located in the district of Ibun, Majalaya Bandung. The product of CV. Maemunah has been exported to Japan. To require fabric export best quality is needed to compete with other country. For to get the best quality of product needs to consider their quality control. In The inspection process still manually used four inspection stations with two workers in each station and average 23 second processing time needed inspection per screen. Therefore, unbalance of production volume with inspection process. The effect is bottle neck in inspection process. Inspection Process in CV. In this research proposed designing automated fabric inspection using image processing and Fuzzy Logic Model. Undertake extraction using GLCM to get value of autocorrelation, cluster shade and number of object. The proposed fabric inspection using Fuzzy Logic implemented with MATLAB provides better result in identifying fabric defect and optimizing process time. Using 35 data test produces an overall accuracy 82.86% and average process time 2.528 second. Therefore, using automated fabric inspection can decrease process time 17 second

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique

From The late 90th, "Skin Detection" becomes one of the major problems in image processing. If "Skin Detection" will be done in high accuracy, it can be used in many cases as face recognition, Human Tracking and etc. Until now so many methods were presented for solving this problem. In most of these methods, color space was used to extract feature vector for classifying pixels, but the most of them have not good accuracy in detecting types of skin. The proposed approach in this paper is based on "Color based image retrieval" (CBIR) technique. In this method, first by means of CBIR method and image tiling and considering the relation between pixel and its neighbors, a feature vector would be defined and then with using a training step, detecting the skin in the test stage. The result shows that the presenting approach, in addition to its high accuracy in detecting type of skin, has no sensitivity to illumination intensity and moving face orientation.Comment: 9 Pages, 4 Figure

A fuzzy system for detection and classification of textile defects to ensure the quality of fabric production

The aim of this research focuses on construct a computerized system for textile defects detection. The system merges between image processing methods, statistical methods in addition to the Intelligent techniques via Neural Network and Fuzzy Logic. Gabor filters were used to identify edges and to highlight defective areas in fabric images, then to train the neural network on statistical and geometry features derived from fabric images to form the special neural network distinguish and classify defects into the fourteen categories, which are the most common defects in the textile factory.  The proposed work includes two phases. The first phase is to detect the defects in fabrics. The second phase is the classification phase of the defect. At the defect detection stage, a Discrete Cosine Transfer (DCT) converts the images to the frequency domain.  Image features then drawn and introduce them to the Elman Neural Network to detect the existence of defects. In the classification stage, the images are converted to the frequency domain by the Gabor filter and then the image features are extracted and inserted into the back propagation network to classify the fabric defects in those images. Fuzzy logic is then applied to neural network outputs and interference values are used in fuzzy logic to increase final discrimination. We evaluate a distinction rate of 91.4286% .After applying the fuzzy logic to neural network output; the discrimination rate was raised to 97.1428%.

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author