  The upcoming Square Kilometer Array (SKA) will set a new standard regarding
data volume generated by an astronomical instrument, which is likely to
challenge widely adopted data analysis tools that scale inadequately with the
data size. This study aims to develop a new source detection and
characterization method for massive radio astronomical datasets by adapting
modern deep-learning object detection techniques. These approaches have proved
their efficiency on complex computer vision tasks, and we seek to identify
their specific strengths and weaknesses when applied to astronomical data. We
introduce YOLO-CIANNA, a highly customized deep-learning object detector
designed specifically for astronomical datasets. This paper presents the method
and describes all the low-level adaptations required to address the specific
challenges of radio-astronomical images. We demonstrate this method's
capabilities using simulated 2D continuum images from the SKAO SDC1 dataset.
Our method outperforms every other published result on the specific SDC1
dataset. Using the SDC1 metric, we improve the challenge-winning score by
+139\% and the score of the only other post-challenge participation by +61\%.
Our catalog has a detection purity of 94\% while detecting 40 to 60 \% more
sources than previous top-score results. The trained model can also be forced
to reach 99\% purity in post-process and still detect 10 to 30\% more sources
than the other top-score methods. It is also capable of real-time detection,
with a peak prediction speed of 500 images of 512x512 pixels per second on a
single GPU. YOLO-CIANNA achieves state-of-the-art detection and
characterization results on the simulated SDC1 dataset. The method is open
source and included in the wider CIANNA framework. We provide scripts to train
and apply this method to the SDC1 dataset in the CIANNA repository.
