LED Chip Defect Detection Method Based on a Hybrid Algorithm

Abstract

LED is an extremely important energy-saving lighting products, which has greatly facilitated human life. Meanwhile, it also makes a positive contribution to global carbon neutrality and carbon peaking. Defect detection is a vital part of the production process to control the quality of LED chips. The traditional methods use a microscope for manual visual inspection, which is time-consuming and has inconsistent testing standards, low efficiency, and other deficiencies. To solve these problems, a hybrid algorithm based on geometric computation and a convolutional neural network is proposed for LED chip defect detection. The method takes advantage of the dimensionality reduction of geometric computation to perform coarse detection of defects on preprocessed chip lithography graphs in the form of grid segmentation, which realizes fast coarse screening of large-scale chip samples and reduces postcomputational costs. The convolutional neural network model is used for the secondary fine detection of “suspected defective” chips after geometric coarse screening, and the SPP (spatial pyramid pooling) network model is improved by directly introducing the original feature map into the SPP pooling layer for summation to enhance the global and local feature information of the output feature map. Furthermore, we construct an LED chip image acquisition platform using a high-frequency multimagnification zoom lens, collect training samples of defective chips, and increase the number of samples through image processing techniques. The research introduces the R-CNN, SDD, and YOLO methods to evaluate the superiority of our method in a number of experiments. The experimental results show that our algorithm proposed in this paper has an average precision (AP) of 96.7% for large-scale chip detection with a low defect rate. Compared with other methods, the testing mean average precision (mAP) is 10.39% higher than traditional YOLO v2. The testing mIoU is also 3.63% higher than traditional YOLO v5, the detection speed is also significantly improved, and it has good robustness.

Applying Advanced Lightweight Architecture DSGSE-Yolov5 to Rapid Chip Contour Detection

“…This paper focuses on lightweight architecture proposed to achieve high-speed object detection. The method proposed in earlier papers [8][9][10][11][12] was to make the model more lightweight and improve the speed of inference. These works are consistent with the purpose of this study, but we can provide better performance and accelerate inference.…”

“…They simulated different illumination situations to verify the robustness of the proposed method. Zheng et al [12] introduced a hybrid algorithm of the R-CNN, SDD, and YOLO methods based on geometric computation and a convolutional neural network for LED chip defect detection. The experimental results showed that this algorithm has an average precision (AP) of 96.7% for large-scale chip detection with a low defect rate.…”