| Citation: | LIU Xin-liang, ZHANG Long-quan, LENG Sheng, WANG Jing-qiu, WANG Xiao-lei. An autofocus algorithm for fusing global and local information in ferrographic images[J].Chinese Optics.doi:10.37188/CO.2023-0124
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To address the issues of large error and slow speed of manual focusing in ferrographic image acquisition, we propose an autofocus method for fusing global and local information in ferrographic images.
This method includes two stages. In the first stage, the global autofocus stage, Convolutional Neural Networks (CNN) extract the feature vectors of the whole image, and the Gate Recurrent Unit (GRU) fuses the features extracted in the focus process to predict the global defocusing distance, which serves as coarse focusing. In the local autofocus stage, the system exacted the feature vector of the wear particle and employs the GRU to fuse the current features with those extracted in the previous focusing process. The resulting fused data predicts the current defocusing distance based on the information of the thickest particle, which facilitates fine focusing. Moreover, we propose a determination method for autofocus direction using Laplacian gradient function to improve autofocus accuracy.
Experimental results indicate an autofocus error of 2.51 μm on the test set and a focusing accuracy of 80.1% with a microscope depth of field of 2.0 μm. The average autofocus time is 0.771 s.
The automatic ferrographic image acquisition system exhibits excellent performance and offers a practical approach for its implementation.
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