Design of Object Segmentation and Feature Extraction Based On Deep Learning for AFM Image Processing and Analysis System

In this paper, an intelligent processing system based on deep learning is constructed to improve the accuracy and automation of data analysis based on the key issues of atomic force microscope image analysis. Aiming at the problem that the traditional scanning method is limited by imaging speed, this paper optimizes the scanning trajectory planning to improve the imaging speed and reduce the influence of deformation by enhancing the continuity and stability of the path. Aiming at the height deviation and structure distortion caused by environmental interference, a robust correction mechanism is proposed, which combines signal optimization and mathematical segvbrfvgsr combination method to effectively compensate image distortion and improve the reliability of morphometric measurement. In terms of target region extraction, a deep learning model integrating multi-level information is constructed to strengthen feature expression and boundary recognition, improve segmentation accuracy and ensure the accuracy of morphological analysis. In this paper, an AFM image set containing multiple types of labeled data is constructed at the same time. The experimental results verify the superiority of the system in object detection and parameter calculation, which provides an intelligent solution for micro-scale morphological analysis and lays a technical foundation for high-precision data processing.