Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.
Vision-based inspection has been studied and applied in manufacturing processes. The aim of vision-based inspection is to classify objects or products on the basis of vision features instead of manual inspection in industrial quality control. Several methods have been proposed to address industrial inspection. Weyrich et al. [
The contributions of this study are summarized as follows. During industrial inspection, the proposed technique employs a dynamic feature-based strategy on the basis of SVM results and suitably tunes the selection of feature-based schemes for inspecting hybrid blurred/multiple objects. In object classification, the dynamic strategy effectively recognizes objects in manufacturing regardless of blurred or multiple samples. Finally, the dynamic selection algorithm applies suitable feature-based schemes to improve the conventional methods for inspecting hybrid objects.
The remainder of this paper is organized as follows. Section
This section describes the existing approaches, which include feature-based schemes and image segmentations, related to the proposed method and finally addresses the differences between the proposed method and the existing approaches.
Recent studies have investigated feature-based methods in image processing and computer vision. Discrete wavelet transform (DWT) is a frequently used technique because of its satisfactory feature extraction engendered by its space-frequency localization and multiresolution characteristics. Huang et al. [
Region growing-based image segmentation techniques have been studied in recent years. Zhang et al. [
Considering previous studies, the existing methods including the feature-based methods (DWT, INV, and SWT), region growing segmentation, and SVM classification are similar to those used in this study. However, the proposed method entails solving the detection problem by using suitable multifeature-based schemes rather than single-feature-based one as distinct from the existing methods. The study employed a dynamic feature-based strategy combined with ARG/LARG-based classification for effectively performing hybrid blurred/multiple object discrimination in manufacturing processes. This dynamic strategy selects the DWT, SWT, INV, and EFD schemes to solve the extraction problem inherent in single-feature-based methods when concurrently detecting hybrid blurred/multiple objects. This study also applied the proposed blurred/multiple object detection system that employs suitable inspection schemes combined with ARG/LARG-based classification methods for inspecting single/multiple objects to minimize inspection times. Finally, this study quantitatively compared inspection methods in the manufacturing field.
This section describes the dynamic feature-based method including the SVM algorithm and feature-selection algorithm and then introduces the hybrid blurred/multiple object detection system for inspecting hybrid objects.
This paper proposes a selection algorithm to dynamically select suitable feature-based schemes for effectively inspecting hybrid blurred/multiple objects. As illustrated in Figure
Local subregions of
Figure
Different types for hybrid blurred/multiple object detection.
Figure
SVM algorithm.
For every SVM classifier, two parameters, namely, parameter
The EFD of an inspection image was used for the SVM classification. According to the 3 × 3 mask depicted in Figure
(a) 3 × 3 mask, (b) patterns 1–4, and (c) patterns 5–8.
After all image pixels were processed using the aforementioned procedure, the edge was classified using feature vectors
Accuracy test for different combinations of the parameter
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5 | 97 | 98 | 98 | 98 | 98 | 98 | 97 |
7 | 98 | 98 | 99 | 99 | 99 | 98 | 97 |
9 | 98 | 99 | 100 | 100 | 99 | 99 | 98 |
11 | 98 | 99 | 100 | 100 | 100 | 99 | 98 |
13 | 98 | 98 | 99 | 100 | 100 | 99 | 98 |
15 | 97 | 98 | 99 | 99 | 99 | 98 | 97 |
17 | 97 | 98 | 98 | 98 | 98 | 98 | 97 |
Classification results using different sample sizes for each class.
Sample size | Training | Validation | Accuracy rates (%) |
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70 | 20 | 50 | 88 |
80 | 30 | 50 | 92 |
140 | 40 | 100 | 94 |
280 | 80 | 200 | 100 |
400 | 100 | 300 | 100 |
800 | 200 | 600 | 100 |
This section describes the dynamic feature-based method and then introduces the feature-selection algorithm used to dynamically select suitable feature-based schemes for effectively inspecting the hybrid objects. Figure
Dynamic feature-based method.
Input the inspection images with the classified object features from the SVM algorithm.
Determine whether the object features are
Implement LARG segmentation if the object features are
Implement the feature-selection algorithm to extract features.
Classify images using SVM/SVMs (Figure
Determine the recognition rate of each adjustable threshold
Terminate the process and obtain a suitable feature-based scheme. In addition, if any
For example, Step
Figure
Different feature-based schemes for
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A | B | C | D |
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0 | DWT | EFD | DWT | EFD |
1 | SWT | N/A | SWT | N/A |
2 | INV | N/A | INV | N/A |
Feature-selection algorithm.
For DWT feature extraction, an image signal is decomposed into various scales at different levels of resolution. The relationships among the DWT coefficients can be expressed as follows:
This section describes the experimental setup and proposed hybrid blurred/multiple object detection system. The technology used in manufacturing classifies hybrid blurred/multiple objects. However, this study further modified the conventional inspection process in manufacturing to effectively classify the hybrid objects in a single image. Detecting hybrid objects has long been a difficult task in manufacturing because single-feature-based methods might fail to precisely extract features when concurrently detecting the hybrid objects. Therefore, this study proposes a hybrid blurred/multiple object detection system as a solution to inspecting single hybrid images.
As an example of industrial inspection, the dynamic feature-based method was applied to inspect eyeglass lenses. Figure
Experimental setup: (a) signal processing unit and (b) inspection device.
Figure
Figure
Proposed hybrid blurred/multiple object detection system.
This section describes the general classification results obtained using the proposed algorithm and applied to detecting hybrid blurred/multiple objects in manufacturing processes. Experiments were conducted to test the accuracy and performance of the proposed algorithm. The major results from each experiment included hybrid object detections, effectiveness of the proposed system, and accuracy and performance of the proposed algorithm. The results revealed that the proposed algorithm can be used as a hybrid blurred/multiple object inspection tool for dynamically selecting suitable feature-based schemes for inspections. The proposed system, which senses hybrid blurred/multiple objects and applies suitable feature-based schemes, could effectively classify hybrid objects in the local subregions of inspection images and solve the problem associated with concurrently inspecting hybrid objects in a single inspection image. Moreover, this study determined that the proposed algorithm outperformed existing methods.
In this study, the proposed algorithm was tested in a general classification. Table
Classes of the samples used in the experiments.
Class location | General samples |
Manufacturing samples |
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12 mm | Degrees of ±0.5° |
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13 mm | Degrees of ±1.5° |
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15 mm | Degrees of ±3.0° |
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17 mm | Degrees of ±4.5° |
(a) Example of type VI ratchet image and the image segmentations for this image obtained using the (b) dynamic feature-based, (c) DWT-based, (d) SWT-based, and (e) INV-based methods.
Table
Selected thresholds for subregions
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Accuracy rates (%) |
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4 |
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90 |
5 |
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92 |
6 |
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94 |
7 |
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92 |
LOO-CV MSE of approximation coefficient
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MSE |
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0.3211 |
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0.1042 |
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0.3415 |
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0.4267 |
The segmented images obtained using DWT were nearly identical for distinct objects (Figure
Comparison of the accuracy rates (%) associated with the proposed algorithm and DWT-based method.
This study compared the proposed algorithm with hybrid DWT-based methods combined with distinct deblurring schemes. The image deblurring schemes presented by Whyte et al. [
Image segmentations for the local subregions
Comparison of the accuracy rates (%) associated with the proposed algorithm and the hybrid DWT-DEB method.
This section describes the test for the availability of the hybrid blurred/multiple object detection system (Figure
To evaluate the DWT and DWT-DEB-based methods, the same block diagram (Figure
Image segmentations for (a) the local subregions
Time-cost function
This paper proposes a dynamic feature-based algorithm for detecting hybrid blurred/multiple objects in manufacturing as a solution to problems encountered in inspecting hybrid images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images and then applies the selected schemes to employ an ARG/LARG-based method to inspect the hybrid objects. The proposed algorithm can effectively classify hybrid objects in the local subregions of inspection images and solve the problem associated with concurrently inspecting hybrid objects in a single inspection image. The results demonstrate that the proposed algorithm can be used as a hybrid blurred/multiple object inspection tool for dynamically selecting suitable feature-based schemes for inspections. Moreover, the hybrid blurred/multiple object detection system can sense hybrid blurred/multiple objects and apply suitable schemes to attain an average recognition rate of 94% (from 92% to 95%). The proposed algorithm outperformed single-feature-based methods (DWT, SWT, and INV) and the hybrid deblurring method (DWT-DEB).
The author has no conflict of interests to declare regarding the publication of this paper.