Surface Defect Detection And Polishing Parameter Optimization Using Image Processing For G3141 Cold Rolled Steel

Traditionally the surface quality inspection especially for metal polishing purpose is perform by human inspectors. Defect detection is a method of nondestructive testing of material and products to detect defects. This study consists of two parts where the first part is applying vision system to detect and measure surface defects that have been characterized to some level of surface roughness. Specimen of G3141 cold rolled steel is used in this research as it represent the actual material applied in local automotive manufacturer. Gray image of scratch defect on metal surface is detected and information about mean gray pixel value (Ga) is interpreted and converted to surface roughness (Ra) measurement. In this study a new technique is developed where the Ga only read on the specific scratch line without considering the whole image. To realize this, automatic cropping algorithm is developed to detect the region of interest and interpret the Ga value. This techniques will enables the polishing to be done at specific scratch defect area without necessary to develop polishing path throughout the whole surface which is time consuming. Second part is to obtain the optimum polishing parameter by using artificial intelligence technique which is able to predict the grit size, polishing time and polishing force parameter to remove the scratch by polishing process. For the purpose of this study, multiple ANFIS or MANFIS have been selected to predict optimum parameter for polishing parameters. Polishing parameter data can be generated by using MANFIS to predict optimum polishing parameters such as grit size, polishing time and polishing force in order to perform polishing process. However due to lack of study done in the field of flat and dry polishing, the polishing parameter data have to be developed. The polishing parameter data for flat and dry polishing is performed by using robotic polishing arm and the experiment runs design by using full factorial design. Results show that the defect detection algorithm able to detect defect only on the scratch area and able to read the Ga value at detected scratch line and transform it to surface roughness measurement at considerably good level of accuracy compared with manual method. Results from MANFIS have shown that the system is able to predict up to 95% accuracy which is considerably high. The overall results from both parts of this research would inspire further advancements to achieve robust machine vision based surface measurement systems for industrial robotic processes specifically in polishing process

Garment smoothness appearance evaluation through computer vision

The measurement and evaluation of the appearance of wrinkling in textile products after domestic washing and drying is performed currently by the comparison of the fabric with the replicas. This kind of evaluation has certain drawbacks, the most significant of which are its subjectivity and its limitations when used with garments. In this paper, we present an automated wrinkling evaluation system. The system developed can process fabrics as well as any type of garment, independent of size or pattern on the material. The system allows us to label different parts of the garment. Thus, as different garment parts have different influence on human perception, this labeling enables the use of weighting, to improve the correlation with the human visual system. The system has been tested with different garments showing good performance and correlation with human perception. © The Author(s) 2012.Silvestre-Blanes, J.; Berenguer Sebastiá, JR.; Pérez Llorens, R.; Miralles, I.; Moreno Canton, J. (2012). Garment smoothness appearance evaluation through computer vision. Textile Research Journal. 82(3):299-309. doi:10.1177/0040517511424530S299309823López, F., Miguel Valiente, J., Manuel Prats, J., & Ferrer, A. (2008). Performance evaluation of soft color texture descriptors for surface grading using experimental design and logistic regression. Pattern Recognition, 41(5), 1744-1755. doi:10.1016/j.patcog.2007.09.011Villette, S. (2008). Simple imaging system to measure velocity and improve the quality of fertilizer spreading in agriculture. Journal of Electronic Imaging, 17(3), 031109. doi:10.1117/1.2956835Neri, F., & Tirronen, V. (2009). Memetic Differential Evolution Frameworks in Filter Design for Defect Detection in Paper Production. Studies in Computational Intelligence, 113-131. doi:10.1007/978-3-642-01636-3_7Carfagni, M., Furferi, R., & Governi, L. (2005). A real-time machine-vision system for monitoring the textile raising process. Computers in Industry, 56(8-9), 831-842. doi:10.1016/j.compind.2005.05.010Wang, W., Wong, Y. S., & Hong, G. S. (2005). Flank wear measurement by successive image analysis. Computers in Industry, 56(8-9), 816-830. doi:10.1016/j.compind.2005.05.009Cho, C.-S., Chung, B.-M., & Park, M.-J. (2005). Development of Real-Time Vision-Based Fabric Inspection System. IEEE Transactions on Industrial Electronics, 52(4), 1073-1079. doi:10.1109/tie.2005.851648Kawabata, S., Mori, M., & Niwa, M. (1997). An experiment on human sensory measurement and its objective measurement. International Journal of Clothing Science and Technology, 9(3), 203-206. doi:10.1108/09556229710168324Fan, J., Lu, D., Macalpine, J. M. K., & Hui, C. L. P. (1999). Objective Evaluation of Pucker in Three-Dimensional Garment Seams. Textile Research Journal, 69(7), 467-472. doi:10.1177/004051759906900701Fan, J., & Liu, F. (2000). Objective Evaluation of Garment Seams Using 3D Laser Scanning Technology. Textile Research Journal, 70(11), 1025-1030. doi:10.1177/004051750007001114Yang, X. B., & Huang, X. B. (2003). Evaluating Fabric Wrinkle Degree with a Photometric Stereo Method. Textile Research Journal, 73(5), 451-454. doi:10.1177/004051750307300513Kang, T. J., Kim, S. C., Sul, I. H., Youn, J. R., & Chung, K. (2005). Fabric Surface Roughness Evaluation Using Wavelet-Fractal Method. Textile Research Journal, 75(11), 751-760. doi:10.1177/0040517505058855Mohri, M., Ravandi, S. A. H., & Youssefi, M. (2005). Objective evaluation of wrinkled fabric using radon transform. Journal of the Textile Institute, 96(6), 365-370. doi:10.1533/joti.2004.0066Zaouali, R., Msahli, S., El Abed, B., & Sakli, F. (2007). Objective evaluation of multidirectional fabric wrinkling using image analysis. Journal of the Textile Institute, 98(5), 443-451. doi:10.1080/00405000701489156Yu, W., Yao, M., & Xu, B. (2009). 3-D Surface Reconstruction and Evaluation of Wrinkled Fabrics by Stereo Vision. Textile Research Journal, 79(1), 36-46. doi:10.1177/004051750809049

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

Parsing Occluded People by Flexible Compositions

This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flexible models. By exploiting part sharing, we show that this inference can be done extremely efficiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked "We Are Family" Stickmen dataset and obtain significant performance improvements over the best alternative algorithms.Comment: CVPR 15 Camera Read

Forward Vehicle Collision Warning Based on Quick Camera Calibration

Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles. In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance priori is proposed to improve the precision. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc