A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

Abstract

Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-Nearest Neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved Voxel density strategy for KNN is proposed to speed up the effect for point filtering. Then, the improved clustering segmentation strategy is applied to distinguish point clouds with defect features. The outline fitting algorithm based on least square method is applied to determine geometric features of each surface defects. Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application.

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“…A novel defect detection approach based on K-nearest neighbour (KNN) and Euclidean clustering segmentation to identify the surface defects of lithium batteries is proposed by Liu. et al [30], and an industrial application example of lithium battery production is demonstrated, which meets the industrial application requirements. Small target detection is a challenge for deep learning-based approaches, and efficient model deployment to mobile and embedded devices requires lightweight models that are trainable, effective and have fast detection speeds.…”