  The recent advent of deep artificial neural networks has resulted in a
dramatic increase in performance for object classification and detection. While
pre-trained with everyday objects, we find that a state-of-the-art object
detection architecture can very efficiently be fine-tuned to work on a variety
of object detection tasks in a high-power laser laboratory. In this manuscript,
three exemplary applications are presented. We show that the plasma waves in a
laser-plasma accelerator can be detected and located on the optical
shadowgrams. The plasma wavelength and plasma density are estimated
accordingly. Furthermore, we present the detection of all the peaks in an
electron energy spectrum of the accelerated electron beam, and the beam charge
of each peak is estimated accordingly. Lastly, we demonstrate the detection of
optical damage in a high-power laser system. The reliability of the object
detector is demonstrated over one thousand laser shots in each application. Our
study shows that deep object detection networks are suitable to assist online
and offline experiment analysis, even with small training sets. We believe that
the presented methodology is adaptable yet robust, and we encourage further
applications in high-power laser facilities regarding the control and
diagnostic tools, especially for those involving image data.
