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• W2969900370 abstract "Detecting the defects of a battery on the surface and edge has always been difficult, especially for concave and convex ones, thereby seriously affecting its quality. Thus, sub-regional Gaussian and moving average filtering are innovatively proposed in this study considering the effect of the nonuniform background illumination of the battery edge and the difference between the edge background and the internal surface defects of the battery. The battery surface image is divided into two areas, namely, edge area W 1 and inner area W 2 . Gaussian and moving average filtering are carried out row-by-row and column-by-column in the inner area W 2 and the edge area W 1 , respectively. The algorithm is tested on 600 battery samples that mainly possess concave and convex defects. The proposed method has higher detection accuracy and lower omission detection rate than the traditional unpartitioned processing method, especially in detecting the accuracy of edge defects. The accuracy rates were approximately 20% higher than that obtained by the traditional processing algorithm. The proposed method has remarkable real-time performance that can process four 8192 × 10,240 pixel battery images per second, thereby meeting the industrial production line speed requirements while satisfying accuracy. The proposed method has been applied in actual production for defect inspection." @default.
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• W2969900370 date "2019-08-19" @default.
• W2969900370 modified "2023-09-27" @default.
• W2969900370 title "Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter" @default.
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• W2969900370 doi "https://doi.org/10.3390/app9163418" @default.
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