Applications of object detection networks in high-power laser systems and experiments

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 vari...

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Bibliographic Details
Published in:High power laser science and engineering 2023-01, Vol.11, Article e7
Main Authors: Lin, Jinpu, Haberstroh, Florian, Karsch, Stefan, Döpp, Andreas
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Boxes
Damage detection
Electron beams
Electron energy
Energy spectra
Experiments
High power lasers
Laboratories
Laser damage
Lasers
Machine learning
Neural networks
Object recognition
Plasma density
Plasma waves
Sensors
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Summary: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 paper, 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 1000 laser shots in each application. Our study shows that deep object detection networks are suitable to assist online and offline experimental analysis, even with small training sets. We believe that the presented methodology is adaptable yet robust, and we encourage further applications in Hz-level or kHz-level high-power laser facilities regarding the control and diagnostic tools, especially for those involving image data.
ISSN:2095-4719
2052-3289
DOI:10.1017/hpl.2023.1