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With the expansion in light-emitting diode (LED) lighting market and technology, control of product quality has become the focus of LED development. To achieve high online production capacity, automated quality detection with object image has been employed for comparison using mostly the standard templates. However, the resulting poor fitting causes misjudgment of the detection system. This study proposes an adaptive template method to improve the system fitting, reduce the system misjudgment, and enhance the detection efficiency. The severely damaged LED chips were screened out based on their grayscale entropy indices and related coefficient indices, which enhanced the reliability of the adaptive template system and accelerated the overall system detection process. To overcome the displacement and scale changes of the lithographic patterns, the scale-invariant feature transform (SIFT) and Harris-Laplace methods were used for comparison. The scale-invariant feature transform (SIFT) and Harris-Laplace methods were used to search and compare the feature points of the chip patterns, with the aim to overcome the displacement and scale changes of the lithographic patterns and establish the adaptive template in real conditions. The fast correlation coefficient comparison method was compared with the adaptive template comparison method proposed in this study. The results showed that the detection accuracy of the new method was 98.36%, which is 15.79% more accurate than the fast correlation coefficient comparison method. In terms of time performance, the method proposed in this study took 0.08s less to complete the partition defect template. Moreover, the average detection time per chip was reduced by another 1.4s, which improved the efficiency by 30.43%. The adaptive template proposed in this study exclusively establish itself based on different lithographic patterns, thus improving the detection efficiency and accuracy of the LED industry, and raising its market competitiveness in the industry.

With the expansion in light-emitting diode (LED) lighting market and technology, control of product quality has become the focus of LED development. To achieve high online production capacity, automated quality detection with object image has been employed for comparison using mostly the standard templates. However, the resulting poor fitting causes misjudgment of the detection system. This study proposes an adaptive template method to improve the system fitting, reduce the system misjudgment, and enhance the detection efficiency. The severely damaged LED chips were screened out based on their grayscale entropy indices and related coefficient indices, which enhanced the reliability of the adaptive template system and accelerated the overall system detection process. To overcome the displacement and scale changes of the lithographic patterns, the scale-invariant feature transform (SIFT) and Harris–Laplace methods were used for comparison. The scale-invariant feature transform (SIFT) and Harris–Laplace methods were used to search and compare the feature points of the chip patterns, with the aim to overcome the displacement and scale changes of the lithographic patterns and establish the adaptive template in real conditions. The fast correlation coefficient comparison method was compared with the adaptive template comparison method proposed in this study. The results showed that the detection accuracy of the new method was 98.36%, which is 15.79% more accurate than the fast correlation coefficient comparison method. In terms of time performance, the method proposed in this study took 0.08s less to complete the partition defect template. Moreover, the average detection time per chip was reduced by another 1.4s, which improved the efficiency by 30.43%. The adaptive template proposed in this study exclusively establish itself based on different lithographic patterns, thus improving the detection efficiency and accuracy of the LED industry, and raising its market competitiveness in the industry.