Flaw detection of domed surfaces in LED packages by machine vision system

Epoxy-packaging is widely used in light-emitting diode (LED) packages to protect LED chips and magnify the chip light. Surface flaws in LED packages affect not only the appearances of LEDs but also their functionality, efficiency and stability. Due to the high demand for productivity and quality, bare-eye-inspection approach becomes extremely inadequately. Therefore, this research proposes a machine-vision-based system for detecting tiny flaws occurred in the domed surfaces of LED epoxy-packing. We apply grey relational analysis to the frequency components in block discrete cosine transform domain, and significantly attenuate the large-magnitude frequency components that represent the background texture of the surface based on their corresponding grey relational grades. Then, by reconstructing the declined frequency components, we eliminate not only random texture but also uneven illumination patterns and retain anomalies in the restored image. This approach overcomes the difficulties of inspecting tiny flaws from uneven illumination backgrounds. Experimental results show that the proposed method can effectively inspect tiny flaws in LED domed surfaces.