An innovative blemish detection system for curved LED lenses
Highlights
► LED lens with a transparent and curved surface is difficult to detect visual defects. ► This study proposes an innovative blemish detection system to inspect LED lens. ► It applies block DCT, Hotelling’s T-squared statistic, and grey clustering technique. ► Experimental results show that the proposed system achieves high performance.
Introduction
A lens is an optical device with perfect or approximate axial symmetry which transmits and refracts light, converging or diverging the beam. Lenses are typically made of glass or transparent plastic. Optical lenses are transparent components made from optical-quality materials and curved to converge or diverge transmitted rays from an object. These rays then form a real or virtual image of the object. There are many types of optical lenses. Optical lenses are widely used in cell phones, notebooks, automotive lights, digital cameras, scanners, head lamps etc.
A light-emitting diode (LED) is a semiconductor device that emits visible light when an electric current passes through the semiconductor chip. Compared with incandescent and fluorescent illuminating devices, LEDs have lower power requirement, higher efficiency, and longer lifetime. Typical applications of LED components include indicator lights, LCD panel backlighting, fiber optic data transmission, etc. To meet consumer and industry needs, LED products are being made in smaller sizes, which increase difficulties of product inspection. The functions of LED lenses include focusing, beauty, and protection to avoid the waste of light and light pollution. An LED without the assistance of lens focus function cannot project light to the intended location. Therefore, LED lenses are invented to improve the light scattering problems of LEDs and they are widely applied to hand flashlights and traffic lights applications. Fig. 1 shows the common LED lens and LED lens product.
Lens inspection requires special physical conditions, particularly in terms of lighting. In the real working situation, each inspected lens is brought into the inspector’s field of vision. The lenses are round and transparent; the blemish to be inspected could be located on the external surface of the lenses or inside. A lens presents a certain thickness and a certain curvature, both of which vary. At times, lenses provide the same perceptive result as a magnifying glass, and the blemishes are all the more difficult to track down and to locate in the area of the lens. The majority of blemishes are not only very small but also they are extremely diverse and can assume various forms. Fig. 2 presents LED lenses with and without visual blemishes.
Currently, the most common detection methods for LED lens blemishes are human visual inspection. Human visual inspection is tedious, time-consuming and highly dependent on the inspectors’ experiences, conditions, or moods. Erroneous judgments are easily made because of inspectors’ subjectivity and eye fatigues. Difficulties exist in precisely inspecting tiny flaws by machine vision systems because when product images are being captured, the area of a tiny flaw could expand, shrink or even disappear due to uneven illumination of the environment, transparent and curved surfaces of the product, and so on. Seeing the great need for an automated visual detection scheme for LED lens blemishes, we propose an innovative detection system applying block discrete cosine transform (BDCT) and gray clustering technique to overcome the difficulties of traditional machine vision systems.
Section snippets
Blemish inspection methods
Inspection of surface blemishes has become a critical task for manufacturers who strive to improve product quality and production efficiency (Lin and Lin, 2009, Lin et al., 2008). Blemish detection techniques, generally classified into the spatial domain and the frequency domain, compute a set of textural features in a sliding window and search for significant local deviations among the feature values. Latif-Amet, Ertüzün, and Ercil (2000) presented wavelet theory and co-occurrence matrices for
Proposed system
By regarding an input image as a matrix, we can perform the DCT transformation to transform a spatial domain image into the frequency domain. A spatial domain image with equal sized blocks is converted to DCT domain and some representative energy features of each DCT block are extracted. These energy features of each block are integrated by the T-squared statistic and the suspected blemish blocks can be determined by the multivariate statistical method. Then, the grey clustering algorithm based
Implementation and analyses
In this section, we implement the proposed approach and conduct experiments to evaluate its performance in detecting visual blemishes of LED lenses. To strengthen the visibility of the visual blemishes, we make use of the following equipments: a yellow ring lighting device, a USB 2.0 color CCD of ARTRAY company, a lens with 1 to 10 amplifications of changeable focal lengths, and a XYZ electronic control table with a controller. Experiments are conducted on 218 real LED lenses (including 148
Concluding remarks
Machine vision systems improve productivity and quality management, and provide competitive advantages to industries that apply these systems. This research proposes an innovative vision system that applies BDCT, Hotelling’s T-squared statistic, and grey clustering technique for the automatic detection of visual blemishes in curved surfaces of LED lenses. Real LED lenses are used as testing samples, and large-sample experiments are conducted in a real inspection environment to verify the
Acknowledgement
This work was partially supported by the National Science Council (NSC) of Taiwan, under Grant No. NSC 97-2221-E-324-020-MY3.
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