Multidimensional computed measurement for highly accurate PCBA defect detection

Accurate defect detection in industrial automated optical inspection (AOI) is crucial as it directly affects product quality and production efficiency. Although numerous techniques have been developed for industrial defect detection, most of them rely on single-texture image data. This dependence limits the accuracy and robustness of the defect detection due to inadequate optical source information. To overcome the problem of low accuracy owing to the lack of 3D topographic information, a multidimensional information fusion (MIF) module is proposed that fuses texture image and depth image features. The MIF module includes tailored mechanisms to fully extract complementary semantic information from space and channel dimensions. A hierarchical fusion strategy further improves feature integration by enabling higher-layer feature fusion via lower-layer Transformer blocks and efficiently removing redundant features. Afterward, feature extraction is performed on the fused feature map, and output is obtained. To enhance the detection accuracy, the position information mask (PIM) module is introduced for post-processing. The PIM module uses surface mount devices (SMDs) position data from Gerber files to create a position information mask. The mask helps filter out defects that are often misidentified owing to missing design information. The results of the comparative experiments demonstrate that the average accuracy of our method on the printed circuit board assembly (PCBA) defect dataset is 99.93%, which is 5.64% higher than that of conventional YOLOV5. Furthermore, a comprehensive ablation study is conducted to elucidate the contribution of the proposed MIF and PIM modules. It demonstrated that the present model serves as a valuable reference for PCBA surface defect detection.