Long-Tailed Defects Classification Based on Probabilistic Aggregation Network for Light-Emitting Diode Packaging Process

Classifying different types of defects in light-emitting diode (LED) packaging poses a challenging and complex task. The difficulty in collecting diverse defect types presents a challenge characterized by a long-tailed distribution. Furthermore, this challenge is compounded by the complexity of the packing process, which involves a wide range of intricate defect types, making it difficult for the model to learn accurate decision boundaries. To tackle these issues, we propose the probabilistic aggregation network (PraNet) for detecting defects in LED packaging. First, the model learns to perform binary classification between normal and defect, then leverages binary classification confidence as a prior fusion for multiclass decision-making, mitigating the problem of low recall rates in the tail categories of the long-tailed distribution. Furthermore, by adapting the objective of supervised contrastive learning, we construct explicit decision boundaries in the feature space. Finally, we introduce a curriculum learning method to facilitate training coordination among all networks and loss functions. This method is verified on a real industrial process which includes 13 types of defects. Experimental results demonstrate the effectiveness of our proposed PraNet method in LED packaging defect classification task with imbalanced data, achieving an accuracy of 98.36% for normal binary classification and 93.47% for defects multiclass classification.