LAUSR

This paper presents a novel method for lithium-ion battery electrode (LIBE) surface quality assurance. First, based on machine vision, an automatic optical inspection system is developed to check defects on LIBE. In addition, a background normalization algorithm is put forward to preprocess the large-scale LIBE with inhomogeneous thickness in uneven illumination. With the help of the auto-concentration compensation algorithm, flaws can be extracted precisely. Moreover, after characterizing the defects, features machine applied partiality parameter automatic adjustment method and partiality decision rules are exerted for defects accurate classification, which provides near-optimal performance and reduces the complexity of tuning parameters. The proposed method is computationally efficient and satisfies real-time online inspection requirement. Experimental results verify the effectiveness and performance of the proposed method according to the inspection speed and recognition rate.