  Large surveys using modern telescopes are producing images that are
increasing exponentially in size and quality. Identifying objects in the
generated images by visual recognition is time-consuming and labor-intensive,
while classifying the extracted radio sources is even more challenging. To
address these challenges, we develop a deep learning-based radio source
detector, named \textsc{HeTu}, which is capable of rapidly identifying and
classifying radio sources in an automated manner for both compact and extended
radio sources. \textsc{HeTu} is based on a combination of a residual network
(ResNet) and feature pyramid network (FPN). We classify radio sources into four
classes based on their morphology. The training images are manually labeled and
data augmentation methods are applied to solve the data imbalance between the
different classes. \textsc{HeTu} automatically locates the radio sources in the
images and assigns them to one of the four classes. The experiment on the
testing dataset shows an average operation time of 5.4 millisecond per image
and a precision of 99.4\% for compact point-like sources and 98.1\% for
double-lobe sources. We applied \textsc{HeTu} to the images obtained from the
GaLactic and the Galactic Extragalactic All-Object Murchison Wide-field Array
(GLEAM) survey project. More than 96.9\% of the \textsc{HeTu}-detected compact
sources are matched compared to the source finding software used in the GLEAM.
We also detected and classified 2,298 extended sources (including
Fanaroff-Riley type I and II sources, and core-jet sources) above $5\sigma$.
The cross-matching rates of extended sources are higher than 97\%, showing
excellent performance of \textsc{HeTu} in identifying extended radio sources.
\textsc{HeTu} provides an efficient tool for radio source finding and
classification and can be applied to other scientific fields.
