AFCN: An attention‐directed feature‐fusion ConvNeXt network for low‐voltage apparatus assembly quality inspection

Abstract

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.