Optimization Research on Rapid and Accurate Underwater Positioning of Fuel Assemblies Based on SAM Pre-Trained Large Model Intelligent Combination Strategy

Chen Xiangyu; Li Hao; Wang Bingyan; Wang Kun; Wan Hao; Wen Xin

doi:10.13832/j.jnpe.2023.S2.0140

Volume 44 Issue S2

Dec. 2023

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Article Navigation > Nuclear Power Engineering > 2023 > 44(S2): 140-145

Chen Xiangyu, Li Hao, Wang Bingyan, Wang Kun, Wan Hao, Wen Xin. Optimization Research on Rapid and Accurate Underwater Positioning of Fuel Assemblies Based on SAM Pre-Trained Large Model Intelligent Combination Strategy[J]. Nuclear Power Engineering, 2023, 44(S2): 140-145. doi: 10.13832/j.jnpe.2023.S2.0140

Citation:

Chen Xiangyu, Li Hao, Wang Bingyan, Wang Kun, Wan Hao, Wen Xin. Optimization Research on Rapid and Accurate Underwater Positioning of Fuel Assemblies Based on SAM Pre-Trained Large Model Intelligent Combination Strategy[J]. Nuclear Power Engineering, 2023, 44(S2): 140-145. doi: 10.13832/j.jnpe.2023.S2.0140

Citation:

Chen Xiangyu, Li Hao, Wang Bingyan, Wang Kun, Wan Hao, Wen Xin. Optimization Research on Rapid and Accurate Underwater Positioning of Fuel Assemblies Based on SAM Pre-Trained Large Model Intelligent Combination Strategy[J]. Nuclear Power Engineering, 2023, 44(S2): 140-145. doi: 10.13832/j.jnpe.2023.S2.0140

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Optimization Research on Rapid and Accurate Underwater Positioning of Fuel Assemblies Based on SAM Pre-Trained Large Model Intelligent Combination Strategy

doi: 10.13832/j.jnpe.2023.S2.0140

Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2023-07-11
Rev Recd Date: 2023-09-20
Publish Date: 2023-12-30

Abstract

Abstract

Accurate positioning of fuel assembly is the key to successful grasping and nondestructive lifting in the process of reactor refueling. Due to the high requirements for safety and efficiency, it is necessary to realize adaptability and reliability in a changeable environment while ensuring high accuracy in the face of complex underwater environment in the reactor, so as to realize full-automatic accurate positioning. In this study, the object rapid detection model (YOLO) is established, and the natural language processing-based SAM (Segment Anything Model) image segmentation pre-training large model is used for further segmentation. Finally, the post-processing optimization of target graphics is realized through a priori structure, and the positioning accuracy of fuel assembly is improved. This multi-stage combined strategy model achieves algorithmic interpretation of fully automated intelligent positioning process to meet the debugging requirements at the refueling site, while solving the instability problem in the underwater uncertain environment through fast detection-intelligent segmentation-post process to achieve high paradigm and robustness. In the simulated test environment, the model is fast, reliable and highly debuggable. The positioning time of a single assembly is less than 1s, the error is less than 0.5 mm, and the positioning efficiency of refueling is improved by more than 90%, allowing significant reduction of the irradiation dose of operator.
- Reactor fuel assembly,
- Computer vision,
- Image segmentation,
- Object detection,
- Foundation model,
- Reactor refueling process

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References(13)

References

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