Lithium-Ion battery electrode defect detection based on topological filtering and improved canny operator

doi:10.19799/j.cnki.2095-4239.2022.0167

Abstract

Abstract:

The quality of the electrode, as an important component of a lithium battery, is directly related to the electrochemical performance, safety, and service life of the battery. An electrode defect detection algorithm using topology filtering and an improved Canny operator is proposed to address the problems of uneven brightness of electrode images and low defect contrast. First, the filtering template using the topology principle is used to suppress the background noise of the image. To address the problem that the overall gray value of the image is too high, grayscale transformation is applied to correct the grayscale distribution of the image and enhance the image contrast. Second, an improved Canny operator is proposed, which uses bilateral filtering to prevent edge blurring and a multi-scale enhancement algorithm to improve uneven illumination and enhance image details. This is because the background has numerous fine textures and weak edge information. The four-directional Sobel operator is used to calculate the gradient magnitude and direction, which improves edge localization. The high and low thresholds are adaptively determined using the maximum entropy algorithm, avoiding the limitation of edge point determination and improving the effect of edge connection. The experimental research shows that, when compared with other algorithms, this algorithm can properly protect the edge details, accurately and completely extract defect contours (accurate and anti-noise), and has certain practical value.

Key words: appearance visual inspection, lithium-ion battery, topology filtering, improved canny operator

CLC Number:

TP 391.41

Haoran SUN, Yawen LI, Youjun HAN, Yueming HU. Lithium-Ion battery electrode defect detection based on topological filtering and improved canny operator[J]. Energy Storage Science and Technology, 2022, 11(10): 3297-3305.

Figures/Tables 10

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Table 1

PSNR evaluation filtering algorithm"

峰值信噪比	均值滤波	高斯滤波	中值滤波	双边滤波
$P S N R a v e$	22.41119	24.33789	22.15549	25.96187
$P S N R v$	0.03968	0.016902	0.015538	0.020046

Table 1

Fig. 7

Table 2

Fig. 8

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