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.
Highlights
The battery is an essential product that has been widely used in many fields, such as electronics, communication, instrument, transportation, and machinery manufacturing [1,2,3,4]
moving average filtering (MAF) can be regarded as a window of a certain size (N, in this case) that moves along the array that constitutes from the input signal, one element at a time (Figure 7)
Table illustrates the accuracy rates and detection time with different different concave and convex defect inspection methods, because there is almost no researchconcave on the and convex defect methods, is almost no research the surface of the surface defects ofinspection the battery, batterybecause surfacethere images we acquired areonapplied intodefects the current battery, battery surface images we acquired are applied into current popular metal surface defect popular metal surface defect detection algorithms, and thethe performance index is analyzed
Summary
The battery is an essential product that has been widely used in many fields, such as electronics, communication, instrument, transportation, and machinery manufacturing [1,2,3,4]. The demand for a battery with high surface, performance, and quality increases annually given the rapid development of modern science and technology Various defects, such as scratch [5], concave, and convex, appear on the battery’s surface during production due to defects of raw material, rolling equipment, and processing technology [6,7]. We proposed an efficient algorithm for battery surface and edge defect inspection based on sub-regional Gaussian and moving average filtering. The linear array CCD camera continuously scans the battery’s surface
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