An innovative blemish detection system for curved LED lenses

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.