A novel approach to label road defects in video data: semi-automated video analysis

Road defects like potholes have a major impact on road safety and comfort. Detecting these defects manually is a highly time consuming and expensive task. Previous approaches to detect road events automatically using acceleration sensors and gyro meters showed good results. However, these results could be significantly improved with additional usage of image analysis. A large, labeled image data set is required for training and validation. This paper presents a method to automate parts of the labeling task. The method is based on a simple two step approach: at first, an unsupervised algorithm detects possible events based on the acceleration data and filters those video sequences with defects. Second, a human operator decides based on the short video sequences if the event was due to an existing road defect and labels the corresponding area in an image

Subject Unrest

Roll-to-roll manufacturing of micro components based on advanced printing, structuring and lamination of ceramic tapes is rapidly progressing. This large-scale and cost-effective manufacturing process of ceramic micro devices is however prone to hide defects within the visually opaque tape stacks. To achieve a sustainable manufacturing with zero defects in the future, there is an urgent need for reliable inspection systems. The systems to be developed have to perform high-resolution in-process quality control at high speed. Optical coherence tomography (OCT) is a promising technology for detailed in-depth inspection and metrology. Combined with infrared screening of larger areas it can solve the inspection demands in the roll-to-roll ceramic tape processes. In this thesis state-of-art commercial and laboratory OCT systems, operating at the central wavelength of 1.3 µm and 1.7 µm respectively, are evaluated for detecting microchannels, metal prints, defects and delaminations embedded in alumina and zirconia ceramic layers at hundreds of micrometers beneath surfaces. The effect of surface roughness induced scattering and scattering by pores on the probing radiation, is analyzed by experimentally captured and theoretically simulated OCT images of the ceramic samples, while varying surface roughnesses and operating wavelengths. By extending the Monte Carlo simulations of the OCT response to the mid-infrared the optimal operating wavelength is found to be 4 µm for alumina and 2 µm for zirconia. At these wavelengths we predict a sufficient probing depth of about 1 mm and we demonstrate and discuss the effect of rough surfaces on the detectability of embedded boundaries. For high-precision measurement a new and automated 3D image processing algorithm for analysis of volumetric OCT data is developed. We show its capability by measuring the geometric dimensions of embedded structures in ceramic layers, extracting features with irregular shapes and detecting geometric deformations. The method demonstrates its suitability for industrial applications by rapid inspection of manufactured samples with high accuracy and robustness. The new inspection methods we demonstrate are finally analyzed in the context of measurement uncertainty, both in the axial and lateral cases, and reveal that scattering in the sample indeed affects the lateral measurement uncertainty. Two types of image artefacts are found to be present in OCT images due to multiple reflections between neighboring boundaries and inhomogeneity of refractive index. A wavefront aberration is found in the OCT system with a scanning scheme of two galvo mirrors, and it can be corrected using our image processing algorithm.QC 20140428Multilayer (FP7-NMP4-2007-214122

Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

DefectNET: multi-class fault detection on highly-imbalanced datasets

As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data. The prediction results of traditional networks give a bias toward larger classes, which tend to be the background in the semantic segmentation task. This becomes a major problem for fault detection, where the targets appear very small on the images and vary in both types and sizes. In this paper we propose a new network architecture, DefectNet, that offers multi-class (including but not limited to) defect detection on highly-imbalanced datasets. DefectNet consists of two parallel paths, which are a fully convolutional network and a dilated convolutional network to detect large and small objects respectively. We propose a hybrid loss maximising the usefulness of a dice loss and a cross entropy loss, and we also employ the leaky rectified linear unit (ReLU) to deal with rare occurrence of some targets in training batches. The prediction results show that our DefectNet outperforms state-of-the-art networks for detecting multi-class defects with the average accuracy improvement of approximately 10% on a wind turbine

Evaluation of the esthetic properties of developmental defects of enamel: a spectrophotometric clinical study

Objectives. Detailed clinical quantification of optical properties of developmental defect of enamel is possible with spectropho- tometric evaluation. Developmental defects of enamel (DDE) are daily encountered in clinical practice. DDE are an alteration in quality and quantity of the enamel, caused by disruption and/or damage to the enamel organ during amelogenesis. Methods. Several clinical indices have been developed to categorize enamel defects based on their nature, appearance, microscopic features, or cause. A sample of 39 permanent teeth presenting DDE on labial surface was examined using the DDE Modified Index and SpectroShade evaluation. The spectrophotometric approach quantifies L∗ (luminosity), a∗ (quantity of green-red), and b∗ (quantity of blue- yellow) of different DDE. Conclusions. SpectroShade evaluation of the optical properties of the enamel defect enhances clinical understanding of severity and extent of the defect and characterizes the enamel alteration in terms of color discrepancy and surface characterization

A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech