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

Abstract

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.

摘要

激光清洗是一种高度非线性物理过程，为解决激光清洗单模态(例如声学或视觉)检测性能不高， 信息间利用率较低的问题，本文提出了基于激光除漆实验构建多模态特征融合网络模型。通过结合分 段聚合近似以及格拉米角场解决不同模态间异构数据转换与对齐问题。并通过引入注意力机制优化双 路径网络和密集连接网络增强对视觉图像和声波的特征提取与融合，从而实现激光除漆的多模态最优 判别检测。实验结果表明，所构建模型在实验数据集上的验证准确率为99.17%，相比激光除漆单模态 检测最优准确率提高5.77%。通过注意力机制优化特征提取网络，模型准确率提升3.3%。结果验证了 本文所构建的多模态特征融合模型在检测激光除漆检测中具有更好的分类性能，模型能有效融合声波 数据和视觉图像数据，实现激光除漆的准确检测。