Recently, our team has made progress in the field of overlap nano-object recognition using transfer learning
Our team has recently published a paper titled “A novel transfer learning for recognition of overlapping nano object” in the international journal “Neural Computing and Application” (IF: 5.606, Q2 in Chinese Academy of Sciences ranking). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author. A significant contribution to this work was made by master’s students Liu Yuhong, Song Leilei, and Tong Lin.
Despite the rapid development of nanoscience and nanotechnology in various fields, the high cost associated with obtaining a sufficient number of nano-object samples remains a challenge, impeding the advancement of deep learning methods in the materials domain. To address this issue, we have designed a novel method for identifying nano-objects in atomic force microscopy (AFM) images. Firstly, we employ a LOG-based image denoising technique for preprocessing the images. Then, we propose two improved methods for segmenting overlapping objects based on the watershed algorithm. Finally, we establish a transfer learning-based convolutional neural network (CNN) recognition model. This model achieves excellent performance by pretraining on large-scale datasets of handwritten digits and letter shapes, enabling the identification of nano-objects in AFM images. The method proposed in this study effectively tackles the challenge of identifying nano-objects in AFM images, where the availability of small-sample nano-objects is limited.