Abstract
Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed. The position pre-estimation method is used to avoid the interference introduced by the disordered chip arrangement and the large missing area. By modifying the NCC algorithm, matching speed is improved by eight times compared to traditional NCC while matching result is not affected by brightness change. Furthermore, feature enhancement schemes with higher speed and accuracy were designed to identify low-contrast defects. The experimental results showed that the average accuracy reached 99.54%, improved by 0.66% compared to the state-of-the-art method while the inspection missing rate was 0.03%. In addition, the detection time of a single chip was approximately 1.098 ms, which meets the requirements of online detection, and the smallest defect that could be detected was 2 µm. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility.
Highlights
Light-emitting diode (LED) have advantages such as small size, low energy consumption, and fast response time, for which they are widely used in display screens and signal lights in the automotive field
A chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed, which can accurately segment LED chips and stably detect various defects
A chip segmentation method based on position pre-estimation and a modified NCC algorithm, as well as feature enhancement methods are proposed in this study
Summary
LEDs have advantages such as small size, low energy consumption, and fast response time, for which they are widely used in display screens and signal lights in the automotive field. Wavelet transform technology [7,8] is widely used to extract texture, but it only decomposes low-frequency signals while texture features exist in high-frequency band In this regard, Zhang [9] used asymmetric Laplacian mixture model [10] to extract chip region and proposed a method based on multi-order fractional Discrete Wavelet Packet Decomposition (DWPD) to process high-frequency parts. Lin [21] designed a six-layer LED-Net to achieve 95% accuracy on line and scratch defects, and used CAM [22] to locate defect location, but owing to the small number of training samples, types of detectable defects could not be expanded In response to this problem, Chen [23] used affine transformation and generative adversarial networks (GAN) [24] to increase dataset, which enhanced generalization ability of the classifier. The two images were input into the proposed identification algorithm
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