  In an effort to explore high-throughput processing of microscopic image data,
a method based on deep convolutional neural network is proposed. The
state-of-the-art computer vision algorithm, Faster R-CNN, was trained for the
detection of iron (II) phthalocyanines on Se-terminated Au(111) platform
resolved by scanning probe microscopy. The construction of the feature pyramid
enables the multi-scale molecule detection in images of different scales from
10 nm to 50 nm. After the detection, the orientation of each molecule is
measured by a following program. Based on the statistical distribution of the
orientation angles, the preferred adsorption configurations of iron (II)
phthalocyanine on the platform are revealed. This method yields high accuracy
and recall with F1 score close to 1 after optimization of hyperparameters and
training. It is expected to be a feasible solution in the scenarios where
autonomous and high-throughput processing of microscopic image data is needed.
