Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach

Abstract

Alkaline battery defect detection is crucial for ensuring product quality and providing diagnostic feedback. Recently, high-performance deep learning algorithms have been introduced to recognise defects in alkaline batteries. However, the majority of deep learning-based methods overlook the significant data imbalance issues in alkaline battery electrode images, potentially resulting in performance degradation. Therefore, a voting-based recognition algorithm containing three parts is proposed in this study. Firstly, a resampling and training method is developed to provide richer information and stronger constraints. Secondly, a weak classification framework based on an improved convolutional neural network (CNN) is designed to provide fine-grained category representations. Finally, a voting-based prediction approach is proposed to improve accuracy and obtain the final results. Visualisation results demonstrate that the proposed algorithm has a stronger clustering ability and uses voting-based prediction to improve performance. Comparison research shows that the proposed method significantly improves the recall of minority classes and the precision of majority classes and reaches a state-of-the-art F1 score of 0.982 for alkaline battery defect recognition, which is 0.041 higher than the basic CNN model.