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摘要
An algorithm based on locally optimized RANSAC (locally optimized random sample consensus, LO-RANSAC) is recommended for the problems of low trace defect detection accuracy, high false detection rate, and high missed detection rate of scratch defect detection on the surface of a lithium battery. First, in response to the problem of pepper noise, large noises that exist on the scratch defects of lithium batteries, a filtering algorithm based on improved adaptive median filtering and connected domains filtering is proposed. Secondly, to address the problems that the detection accuracy of detecting trace defects does not meet expectations and the false detection and missed detection rates are high, a locally optimized RANSAC algorithm is introduced. Finally, a scratch defect classification based on the LO-RANSAC algorithm is proposed. The experimental results demonstrate that when compared to the standard RANSAC algorithm, the proposed algorithm's average detection accuracy is increased by 5.9%, when compared to the convolution-based neural network algorithm is increased by more than 15%, reaching 98.2%. Among several algorithms, the algorithm achieves the lowest false positive and false negative rate for trace defects. The average detection speed is 1.7 times faster than the standard RANSAC algorithm, with an FPS (frame per second) of 12.49 images detected per second. The proposed algorithm has a high detection accuracy, a low false detection rate, and missed detection rate, and a detection speed that meets real-time detection requirements, allowing it to meet the detection needs of trace defect on the surface of lithium battery pole pieces and solve the problem of automatic detection of trace defects on the surface of lithium battery pole pieces. ? 2024 Energy Storage Science and Technology.
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