Guiding GPT models for specific one-for-all tasks in ground penetrating radar
Tong Zheng, Yiming Zhang, Mao Tao
Abstract
Buried object detection using ground penetrating radar (GPR) benefits from deep neural networks but still faces the problem of condition- and question-limited outputs. This paper presents an approach to conduct “one-for-all” (OFA) tasks in GPR data processing . In the approach, a generative pre-trained transformer (GPT) generates the prompts based on input GPR data and an open-ended question. The question, prompts, and GPR data are fed into a GPT-based large language model to obtain a general-purpose answer. Finally, another GPT model summarizes the answer into a GPR-purpose one. An experiment with 10k GPR samples indicates that the proposed approach exceeds the other OFA models with the A P of 77.35% on visual grounding, the BLEU@4 of 65.72 on grounded captioning, the A c c 1 of 83.09% on visual question answer, and the A c c 1 of 83.64% on object-text matching. Besides, the proposed approach can handle GPR data with different frequencies and civil structures.