سیستم تشخیص نوآورانه لکه برای لنزهای منحنی شکل LED
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2362||2013||9 صفحه PDF||سفارش دهید||5892 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 2, 1 February 2013, Pages 471–479
The functions of LED lenses include focusing, beauty, and protection to avoid the waste of light and light pollution. Nevertheless, LED lens with a transparent and curved surface is more difficult to detect the visual blemishes than electronic and optical components by current computer vision systems. This research proposes an innovative blemish detection system to detect visual blemishes of the curved LED lenses. A spatial domain image with equal sized blocks is converted to discrete cosine transform (DCT) domain and some representative energy features of each DCT block are extracted. These energy features of each block are integrated by the Hotelling’s T-squared statistic and the suspected blemish blocks can be determined by the multivariate statistical method. Then, the grey clustering technique based on the block grey relational grades is applied to further confirm the block locations of real blemishes. Finally, a simple thresholding method is applied to set a threshold for distinguishing between defective areas and uniform regions. Experimental results show that the proposed system achieves a high 95.46% probability of correctly discriminating visual blemishes from normal regions and a low 0.13% probability of erroneously detecting normal regions as blemishes on curved surfaces of LED lenses.
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.
نتیجه گیری انگلیسی
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 performance of the proposed approach. Experimental results show that the proposed method achieves a high 95.46% probability of correctly discriminating visual blemishes from normal regions and a low 0.13% probability of erroneously detecting normal regions as blemishes on curved surfaces of LED lenses. Compared with other traditional methods, this approach has the advantages of higher detection rates, lower false alarm rates, and shorter average processing time. This method not only overcomes the difficulties of inspecting visual blemishes on curved surfaces but also relies on no template matching process. The proposed method is based on feature extraction from BDCT-domain images for blemish detection. Since the computation of multivariate statistic is based on the mean vector and covariance matrix of training samples, the lighting changes may lead to the increase of variation in statistics and result in affecting the effect of blemish detection. It is recommended to re-compute the mean vector and covariance of the training samples when illumination is significantly changed. Future research may extend the proposed method to similar low-contrast blemish detection problems, such as abnormal inspection of medical images and tiny blemish detections of electronic and optical components. This research contributes a solution to a common surface blemish detection problem of optical lenses and offers a computer-aided visual blemish inspection system to meet the inspection and quality control request.