The high precision detection of defects in injection molded products relies on the image features extracted by the model.However,the difficulty in collecting defect samples in injection molded products leads to class imbalance in the training dataset,resulting in a decrease in model performance.A high-precision detection method for defects in injection molded products under unbalanced sample conditions is proposed to address this issue.Using multi-scale convolutional neural networks to extract multi-scale image features,and utilizing image knowledge from other classification tasks,two-stage transfer learning is used to change the sample sampling distribution during model training,thereby improving the model's ability to extract features from defective sample images and enhancing its classification performance.The experimental results show that this method has a high average detection accuracy.In the case of extreme class imbalance(IR=25:1),the detection accuracy reaches 99.26%,which is 3.48～8.46 higher than the three comparative methods,meeting the demand for high-quality injection molded product production.
unbalanced samplesdefect detection in injection molded productsmulti-scale neural networktwo-stage transfer learning
维普
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