A novel approach for surface defect detection 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 the least square method is applied to determine geometric features of each surface defect which are used to classify defect types. Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3-ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application. All these reports exhibited that the industrial visual inspection system with rapid measurement is an effective method and guarantees for accelerating industrial production and manufacturing in the future.