Computer vision for efficient object detection and segmentation in molecular image analysis

Image recognition, classification, and analysis of large sets of high-resolution molecular images are time-consuming and labor-intensive, even for human experts, owing to a lack of standardized approaches. In recent years, machine learning has emerged as a powerful tool for automating image data analysis in materials science. In this work, we developed a computer vision program for efficient object detection and instance segmentation, offering a fast alternative to manual molecular image analysis. By integrating YOLOv9 with an incremental learning strategy and hyperparameter optimization, the system enables accurate detection, classification, and segmentation of molecular species across diverse STM datasets. Our results demonstrate robust performance and minimal forgetting rates across multiple molecular categories, enabling scalable and updatable surface image analysis workflows. We anticipate that computer vision methods will see increasing applications in the image data analysis within the field of on-surface chemistry.