【摘要】 为克服三维形貌信息缺失导致的PCBA缺陷检测精度较低的问题，提出一种融合RGB纹理图像和深度图像特征的缺陷检测方法（MGT-Fusion）。该方法在基于RGB纹理图像传统缺陷检测算法的基础上引入深度图像，以获取丰富的空间形貌信息。设计门控融合模块（GFM）和Transformer编码器融合模块（TFM），以融合2种数据的特征信息。GFM使用双门控注意力机制对2类数据进行浅层融合，以提取对应的互补特征。TFM基于自注意力机制获取2类数据的全局相关性，并进行深层融合。搭建基于结构光相移条纹法的高精度自动光学检测设备，收集深度图和RGB图样本对，完成PCBA缺陷数据集的采集。实验结果表明，所提方法在PCBA缺陷数据集上的平均精度均值为99.89%。此外，进行全面的消融和对比实验，以阐明所提GFM和TFM的贡献，以及该方法的整体先进性。所提方法为PCBA表面缺陷检测提供了有价值的参考。更多还原

【Abstract】 To address the low accuracy of PCBA defect detection caused by the lack of 3D morphological information, an MGT-Fusion defect detection method incorporating both RGB texture and depth image features is proposed. The proposed method enhances traditional RGB texture image-based defect detection by integrating depth images to capture richer spatial and morphological details. Gate fusion module(GFM) and Transformer encoder fusion module(TFM) are designed to effectively fuse features from the two modalities. The GFM employs a dual-gated attention mechanism to perform shallow fusion and extract complementary features, while the TFM leverages a self-attention mechanism to capture global correlations and achieve deep fusion. To support the method, high-precision automatic optical inspection equipment based on a structured light phase-shift fringe technique is developed, enabling the acquisition of both depth and RGB images for constructing a comprehensive PCBA defect dataset. Experimental results demonstrate that the proposed method achieves a mean average precision of 99.89% on the dataset. Furthermore, comparative and ablation experiments are conducted to assess the individual contributions of the GFM and TFM, confirming the effectiveness and advancement of the overall approach. This method offers a valuable reference for improving surface defect detection in PCBA applications.更多还原