  Topological defects play a key role in the structures and dynamics of liquid
crystals (LCs) and other ordered systems. There is a recent interest in
studying defects in different biological systems with distinct textures.
However, a robust method to directly recognize defects and extract their
structural features from various traditional and nontraditional nematic systems
remains challenging to date. Here we present a machine learning solution,
termed Machine Eye for Defects (MED), for automated defect analysis in images
with diverse nematic textures. MED seamlessly integrates state-of-the-art
object detection networks, Segment Anything Model, and vision transformer
algorithms with tailored computer vision techniques. We show that MED can
accurately identify the positions, winding numbers, and orientations of $\pm
1/2$ defects across distinct cellular contours, sparse vector fields of nematic
directors, actin filaments, microtubules, and simulation images of Gay--Berne
particles. MED performs faster than conventional defect detection method and
can achieve over 90\% accuracy on recognizing $\pm1/2$ defects and their
orientations from vector fields and experimental tissue images. We further
demonstrate that MED can identify defect types that are not included in the
training data, such as giant-core defects and defects with higher winding
number. Remarkably, MED provides correct structural information about $\pm 1$
defects, i.e., the phase angle for $+1$ defects and the orientation angle for
$-1$ defects. As such, MED stands poised to transform studies of diverse
ordered systems by providing automated, rapid, accurate, and insightful defect
analysis.
