Identifying and counting individual particles is an important component of many studies in various explorations. In the paper we present the results of the application of deep learning methods for the automated recognition of platinum nanoparticles deposited on highly oriented pyrolytic graphite (HOPG) on images obtained by scanning tunneling microscopy (STM). We used the neural network CascadeRCNN. The training was performed on a data set containing 10 STM images with 1918 nanoparticles. Five images containing 2052 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in verification set with 50.8% accuracy. Nanoparticles are specified as distinct contours, which are necessary for further determination of the particles dimensions (size, height etc). The obtained results were compared with the possibilities of other software products. The advantage of using deep machine learning methods for automatic particle recognition is clearly shown.