A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Abstract

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.

Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

“…This method overcomes the problems that arise from the concurrent use of multiple sensing devices. To detect blurred or multiple objects that constitute hybrid images, a dynamic algorithm that employs feature scheme selection was proposed for classifying the mentioned objects constituting the images [15]. The algorithm can engage in the dynamic selection of suitable feature extraction schemes for hybrid object classification and detection.…”

“…The approach uses a PCA-based method and an algorithm based on edge feature description (EFD) to detect patterns from tactile and optical measurements, respectively. Other studies have detected objects using an image segmenting technique [14], a feature scheme selection algorithm [15], and a tactile and optical measurement scheme [16]. In contrast to the aforementioned studies, the current study proposes a PCA-integrated algorithm for identifying suitable image features for effectively detecting objects.…”

“…Framework. The proposed processing framework used for identifying suitable image features can be an alternative to approaches that entail selecting feature-based schemes [15] for effective pattern detection. Figure 1 depicts a schematic illustrating the processing framework; this framework enables the utilization of the SVM classification results for identifying suitable image features.…”

“…Code the image pixel (PIS), EFD, discrete wavelet transform (DWT), spherical wavelet transform (SWT), and moment invariants (INV) feature extraction methods as {0, 1, 2, 3, 4}, and set = {0, 1, 2, 3, 4} sequentially. The PIS method can be used to modify the other extraction methods (EFD, DWT, SWT, and INV) [15] for precise feature extraction.…”

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

“…EFD-based extraction employs an edge feature description for the ARG-segmented image. Edge pixels in the segmented images typically belong to one of the eight possible edge patterns [9]. After all pixels in an image have been processed, the edge is classified using edge feature vectors.…”