DNA
origami is a milestone in DNA nanotechnology. It is robust
and efficient in constructing arbitrary two- and three-dimensional
nanostructures. The shape and size of origami structures vary. To
characterize them, an atomic force microscope, a transmission electron
microscope, and other microscopes are utilized. However, the identification
of various origami nanostructures heavily depends on the experience
of researchers. In this study, we used the deep learning method (improved
Yolox) to detect multiple DNA origami structures and estimate their
yield. We designed a feature enhancement fusion network with the attention
mechanism, and related parameters were researched. Experiments conducted
to verify the proposed method showed that the detection accuracy was
higher than that of other methods. This method can detect and estimate
the DNA origami yield in complex environments, and the detection speed
is in the millisecond range.