In the production of low-voltage apparatus, assembly quality inspection is of great relevance for ensuring the final quality of the entire product. With the continuous improvement of production efficiency and people's requirements for production quality, traditional manual inspection methods can no longer meet the quality inspection requirements. In this paper, an Attention-guided Feature-fusion ConvNeXt Network (AFCN) for the automated visual inspection is proposed. By embedding the attention mechanism of the Coordinate Attention block into the residual channel of the ConvNeXt block, the position-aware information and features of the low-voltage apparatus images can be effectively captured to locate the quality problems. Then, an improved attention feature fusion module is adopted to merge the output features at different stages, which introduces a 3D non-parameter attention SimAM block and adapts output accordingly. Therefore, this model can capture the key information of the feature map in a coordinated way in terms of channel and position, fully integrating multiscale features and obtaining contour texture information and semantic information of the low-voltage apparatus. Experiments show the proposed approach can effectively classify defective and normal products.

中文翻译：

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AFCN：用于低压电器装配质量检测的注意力导向特征融合 ConvNeXt 网络

在低压电器的生产中，装配质量检验对于保证整个产品的最终质量具有重要意义。随着生产效率的不断提高和人们对生产质量要求的不断提高，传统的人工检验方法已经不能满足质量检验要求。在本文中，提出了一种用于自动视觉检测的注意力引导特征融合ConvNeXt网络（AFCN）。通过将坐标注意块的注意力机制嵌入到ConvNeXt块的残差通道中，可以有效捕获低压设备图像的位置感知信息和特征，以定位质量问题。然后，采用改进的注意力特征融合模块来合并不同阶段的输出特征，引入3D非参数注意力SimAM模块并相应地调整输出。因此，该模型能够在通道和位置上协调捕获特征图的关键信息，充分融合多尺度特征，获取低压电器的轮廓纹理信息和语义信息。实验表明，该方法可以有效地对缺陷产品和正常产品进行分类。