Inline inspection of textured plastics surfaces

Shih

et al. 2020

IEEE Access

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.

Section: Introductionmentioning

confidence: 99%

Novel Framework for Optical Film Defect Detection and Classification

Shih

et al. 2020

IEEE Access

“…One mature application of AOI is the inspection of the printed circuit board [1][2][3][4]. Recently, the use of AOI-based defect inspection systems has been extended to several industries, such as steel [5][6][7][8][9][10], textile and garment [11][12][13], solar cells [14][15][16][17][18], plastic [19,20], and liquid crystal display (LCD) [21][22][23][24] [ [21][22][23][24] industries. Recently, glass is being used increasingly in various applications, and thus, the glass industry is growing rapidly.…”

Section: Introductionmentioning

confidence: 99%

Automatic Defect Inspection for Coated Eyeglass Based on Symmetrized Energy Analysis of Color Channels

et al. 2019

Symmetry

Nowadays, eyeglassesare used for vision correction as well as in the fashion industry. Eyeglasses have become more expensive and the eyewear industry has grown rapidly, thereby requiring the development of advanced coating technologies. However, defect detection by visual inspection in the manufacturing process of eyeglass coatings is difficult. To solve this problem, we propose the coated eyeglass defect detection system framework based on machine vision for real-time inspection. First, we locate and extract regions of interest (ROI) ofthe coated eyeglass by adopting cross-projection based on symmetrizedenergy analysis. Next, we propose an efficient method based on the symmetrized energy analysis of color channelsto enhance defectsin each color channel of the ROI of the coated eyeglass. Then, we adoptsymmetrized cross-projection energy analysis for locating defective areas inside the ROI of the coated eyeglass. Finally, we compare the defect detection resultsfor the coated eyeglass with the standard manufacturingquality. An experiment is conducted using real data collected froma Taiwanese eyeglass factory to validate the performance of the proposed framework. This framework achieves a 100% defect detection rate, demonstrating that it is valid and useful for inspecting coated eyeglasses in industries.

“…AOI is an essential tool using optics to capture images of an object under test in an integrated test strategy that ensures costs are kept at the lowest possible by enabling fast and accurate fault-detection in the early period of the production chain. Recently, AOI has been increasingly used for automatic defect detection in several industrial fields, such as printed circuit board [1], railway [2], plastics [3], steel [4,5], glass bottle bottom [6], solar cell [7,8], fabric [9], and planar products [10]. However, most of these systems only detect the defects on the flat surface and are not useful for ski goggles lenses with a spherical surface.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification

et al. 2019

Sensors

No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.