Test method of laser paint removal based on multi-modal feature fusion

Laser cleaning is a highly nonlinear physical process for solving poor single-modal (e.g., acoustic or vision) detection performance and low inter-information utilization. In this study, a multi-modal feature fusion network model was constructed based on a laser paint removal experiment. The alignment of heterogeneous data under different modals was solved by combining the piecewise aggregate approximation and gramian angular field. Moreover, the attention mechanism was introduced to optimize the dual-path network and dense connection network, enabling the sampling characteristics to be extracted and integrated. Consequently, the multi-modal discriminant detection of laser paint removal was realized. According to the experimental results, the verification accuracy of the constructed model on the experimental dataset was 99.17%, which is 5.77% higher than the optimal single-modal detection results of the laser paint removal. The feature extraction network was optimized by the attention mechanism, and the model accuracy was increased by 3.3%. Results verify the improved classification performance of the constructed multi-modal feature fusion model in detecting laser paint removal, the effective integration of acoustic data and visual image data, and the accurate detection of laser paint removal.

Automated summary

In this study, a multi-modal feature fusion network model was constructed based on a laser paint removal experiment, and the attention mechanism was introduced to optimize the detection accuracy. The experimental results showed that the constructed model performed better than the single-modal detection and the accuracy was further improved by optimizing the feature extraction network through the attention mechanism.