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Battery Panel Defect Detection Method Based on Deep Convolutional Neural Network
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DOI：10.1109/wcsp.2019.8928008.png)
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Abstract
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Defect detection of product surface can be made manually or automatically utilizing pattern recognition. Traditional methods usually suffer from low efficiency, high cost, sensitivity to environmental changes, and low detection accuracy. This paper proposes a deep convolutional neural network (DCNN) approach for battery panel defect detection. The training data can be collected by taking images for battery panels from an actual production line. Usually, this training data suffers from many problems such as poor image quality, underrepresentation of defective samples, and different types of defects. We use data enhancement techniques to preprocess the acquired images to obtain as many different samples as possible. Furthermore, we utilized the improved Focal Loss function to deal with prominent problems such as small samples and non-uniform sample data sets. The method achieved good results in the experiment.
Keywords:
deep convolutional neural network
defect detection
unbalanced data classification
focal loss
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