Automated Visual Inspection of Glass Bottle Bottom With Saliency Detection and Template Matching

Zhou, Xianen; Wang, Yaonan; Xiao, Changyan; Qing, Zhu; Lu, Xiao; Zhang, Hui; Ge, Ji; Zhao, Haitao

doi:10.1109/tim.2018.2886977

Cited by 69 publications

(23 citation statements)

References 44 publications

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“…Recently, Zhou et al [70] proposed two new saliency detection method-region growing geodesic saliency (RGGS) and region growing Euclidean saliency (RGES)-and template matching with multiscale mean filtering. Their template matching method achieved an 88.83% detection rate, while the RGES method performed with an accuracy of 75.95%.…”

Section: Filter-based Approachesmentioning

confidence: 99%

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Czimmermann

Ciuti

Milazzo

et al. 2020

Sensors

254

100

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This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

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Section: Filter-based Approachesmentioning

confidence: 99%

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Czimmermann

Ciuti

Milazzo

et al. 2020

Sensors

254

100

View full text Add to dashboard Cite

show abstract

“…This method can achieve a significant saliency performance and satisfy the demand for a real-time inspection process [25]. Zhou et al proposed a region growing-based saliency detection method for glass defect detection, which divides the glass image into super-pixels and selects the defect regions according to their saliency values [26].…”

Section: A Saliency Detectionmentioning

confidence: 99%

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Qiu

Tang

et al. 2020

IEEE Access

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Surface defect detection based on computer vision remains a challenging task due to the uneven illumination, low contrast and miscellaneous patterns of defects. Current methods usually present undesirable detection accuracy and lack adaptability for the various scenes. In the paper, the novel uneven illumination surface defects inspection (UISDI) method is proposed to address these issues. First, the multi-scale saliency detection (MSSD) method is proposed to construct a coarse defect map and obtain the corresponding background regions. Second, a novel background similarity prior-based intrinsic image decomposition model (BSIID) is applied to divide the defect image into a non-defective shading layer and a defective reflectance layer. An accelerated optimization solution is proposed to solve the minimization problem of the intrinsic image decomposition model. Last, the enhanced defect image is obtained by filtering the reflectance image and is then utilized to accurately segment the defect region from the coarse defect map. The experiments conducted using four real-world defect datasets demonstrate that the proposed method outperforms state-of-the-art methods.

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“…AOI is an essential tool that employs optics to capture images of an object that is being tested using an integrated test strategy and ensures that costs are kept as low as possible by detecting faults early in the production line. AOI has been increasingly used for automatic defect detection in several industrial fields for the quality control of products, such as printed circuit boards [1]- [3], plastic products [4], steel products [5], [6], glass products [7], [8], solar cells [9], [10], textiles and garments [11]. Besides, AOI has also been used for counting blister cards within drug packages [12] and checking the surface of the lithium-ion battery electrode [13].…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for Optical Film Defect Detection and Classification

Wang

Shih

et al. 2020

IEEE Access

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Currently, liquid crystal displays (LCDs) are the most popular type of flat panel display and are used in most applications. An LCD contains many critical optical film components that are produced in highly automated and precisely monitored facilities throughout the complex manufacturing process. However, defect detection and classification through visual inspection is very difficult during the manufacturing process. To overcome this problem, a novel framework based on machine vision known as the optical film defect detection and classification system is presented for use in the real-time inspection. First, an image acquisition system equipped with a high-resolution camera and custom-made lighting field was designed to obtain a high-quality optical film image. Second, the defects on an optical film were detected using localized cross-projection based on proposed adaptive energy analysis. Finally, the defect images were classified into four types by using the developed classification algorith-point, scratch, foreign material, and stain. The quality of the products yielded after defect detection and classification of the optical film was compared with the standard product quality of the manufacturer. Experiments were conducted using samples collected from the largest manufacturer in Taiwan to validate the performance of the proposed framework. The accurate defect detection rate is 99.6%, the classification accuracy rate is 100%, and the total operation time is short whichonly 6.129s are required on average to perform the inspection for an optical film sample. The results demonstrate that the proposed method is sound and useful for optical film inspection in industries.

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Automated Visual Inspection of Glass Bottle Bottom With Saliency Detection and Template Matching

Cited by 69 publications

References 44 publications

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Novel Framework for Optical Film Defect Detection and Classification

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