Inspection Of Specular Surfaces Research Articles

Computational grayscale dithering phase measuring deflectomerty for accurate specular surface inspection

Specular surface can be precisely measured by phase measuring deflectometry, but the precision is affected by the enlarged virtual image depth range along the specimen. This paper develops a surface adaptative fringe pattern generation method to eliminate the out-of-focus effect, which dithers the fringe pattern, compensates the defocused effect and factorizes the phase-height optimization with the estimated equivalent defocused level segmentally. The accuracy and reliability are verified by quantitative comparison experiments of specular steps and the deformed screen topography reconstruction. The experimental results demonstrate that the proposed method flexibly reduces the image blurring and the phase-map error caused by the reflected light varies along the surface and the restriction of the camera's field of view.

DeepInspection: Deep learning based hierarchical network for specular surface inspection

• Image fusion based on deflectometry principle can enhance the contrast between defective and non-defective regions. • A benchmark dataset of specular surface defects was constructed for algorithms evaluation and comparison. • An end-to-end attention-based network framework for automatic surface inspection is proposed. • Proposed framework can maintain performance dealing with extremely unbalanced pixel classes. • Quantitative and qualitative tests show superior performance compared to the state-of-art methods. Automated defect detection on specular vehicle surface with limited features (of up to 0.7 mm in diameter or width) and extremely unbalanced pixel classes is a still challenge of product quality control in automotive industry. The traditional defect inspection on specular surface is usually performed by inspectors, which is subjective, unstable and unquantified. Also, due to the limited features of isolated defect regions and hand-crafted feature extraction models may not be able to coordinated with each other, it is difficult for traditional methods to achieve comparable learning performance with the deep network. To alleviate these problems, a novel end-to-end attention-based fully convolutional neural network framework -DeepInspection is proposed for automated defect inspection on specular surface. Specifically, a sequence fusion algorithm through the principle of deflectometry is introduced to enhance the contrast between defective regions (pixels with lower intensity) and non-defective regions (pixels with higher intensity). Then, an attention-based deep convolutional neural network (DCNN) with Atrous Spatial Pyramid Pooling (ASPP) was proposed to capture local-to-global feature presentation from pre-processed fused images. To verify the robustness and effectiveness of the proposed method, a benchmark dataset called DeepInspection160 with 160 manually labeled images is established. Although the defective pixels only account for 0.561% in the DeepInspection160 dataset, the proposed DeepInspection framework still surpasses several state-of-the-art specular surface inspection methods which achieves F1 score over 0.7513 (pixel level) and 0.8055 (individual connected components) on the proposed challenging dataset.

Visually Inspecting Specular Surfaces: A Generalized Image Capture and Image Description Approach

Image capturing and image content description can be regarded as the two major steps of a computer vision process. This paper focuses on both within the field of specular surface inspection, by generalizing a previously defined stripebased inspection method to free-form surfaces on the basis of a specific stripe illumination technique and by outlining a general feature-based stripe image characterization approach by means of new theoretical concepts. One major purpose of this paper is to propose a general stripe image interpretation approach on the basis of a three-step procedure: 1) comparison of different image content description techniques, 2) fusion of the most appropriate ones, and 3) selection of the optimal features. It is shown that this approach leads to an increase in the classification rates of more than 2 percent between the initial fused set and the selected one. The new contributions encompass 1) the generalization of a cylindrical specular surface enhancement technique to more complex specular geometries, 2) the generalization of the previously defined stripe image description by using the same number of features for the bright and the dark stripes, and 3) the definition of an optimal, in terms of classification rates and computational costs, stripe feature set.

An Image Content Description Technique for the Inspection of Specular Objects

This paper proposed an image content description method within the context of specular surface inspection. Such a method is based on a preliminary research concerning the generation of specific stripe patterns for the visual enhancement of defective surface parts of cylindrical specular objects. The goal of this paper is to address the stripe pattern interpretation within a general approach. For this purpose, different pattern recognition processes, consisting not only of the combination of different image segmentation, feature retrieval, and classification, but also of feature combination and selection, will be considered. Three top-down and one bottom-up approaches are evaluated for retrieving the most appropriate feature sets in terms of highest classification rates. It will be demonstrated that following a combination and appropriate selection of these feature sets, even better rates can be reached. With only half of the initial features, an increase of more than 2% is observable.

Specular surface inspection using structured highlight and Gaussian images

The structured highlight inspection method uses an array of point sources to illuminate a specular object surface. The point sources are scanned, and highlights on the object surface resulting from each source are used to derive local surface orientation information. The extended Gaussian image (EGI) is obtained by placing at each point on a Gaussian sphere a mass proportional to the area of elements on the object surface that have a specific orientation. The EGI summarizes shape properties of the object surface and can be efficiently calculated from structured highlight data without surface reconstruction. Features of the estimated EGI including areas, moments, principal axes, homogeneity measures, and polygonality can be used as the basis for classification and inspection. The structured highlight inspection system (SHINY) has been implemented using a hemisphere of 127 point sources. The SHINY system uses a binary coding scheme to make the scanning of point sources efficient. Experiments have used the SHINY system and EGI features for the inspection and classification of surface-mounted-solder joints. >

Structured highlight inspection of specular surfaces

An approach to illumination and imaging of specular surfaces that yields three-dimensional shape information is described. The structured highlight approach uses a scanned array of point sources and images of the resulting reflected highlights to compute local surface height and orientation. A prototype structured highlight inspection system, called SHINY, has been implemented. SHINY demonstrates the determination of surface shape for several test objects including solder joints. The current SHINY system makes the distant-source assumption and requires only one camera. A stereo structured highlight system using two cameras is proposed to determine surface-element orientation for objects in a much larger field of view. Analysis and description of the algorithms are included. The proposed structured highlight techniques are promising for many industrial tasks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>