| dc.description.abstract | Electronic components are the fundamental elements of all electronic products. The quality of electronic components deeply affects the quality of electronic products. Therefore, keeping the quality of electronic components soldered on printed circuit boards is one of the important issues for the relevant industry tasks. Any manufacturing process inevitably encounters abnormal situations, so detecting defects in electronic components on printed circuit boards is a crucial aspect in ensuring the outgoing quality of printed circuit board assemblies (PCBAs).
In recent years, there is a remarkable advancement in the development of deep learning techniques, they demonstrated outstanding performance in various industries. The fields of automated optical inspection (AOI) and automated visual inspection (AVI) also energetically engage the technique to simultaneously improve the defect detection rate and screening rate of products.
Deep learning techniques have been used for detecting electronic component defects on printed circuit boards include recognition, detection, segmentation, anomaly detection, etc. In this studying, we focus on the application of semantic segmentation technique to identify and classify the defective regions of electronic components on printed circuit boards.
We modified the Harmonic DenseNet MSEG (HarDNet-MSEG) for defect segmentation and classification in X-ray images of electronic components. The properties of this studying include: i. The decoder architecture was designed in the form of UNet++. It utilized four levels of resolution feature maps, which facilitated stable detection of small defects and more accurate boundaries. ii. The Receptive Field Blocks (RFBs) module was modified by reducing complex convolutions. This modification was beneficial for capturing small-scale features effectively. iii. An attention module was added to the deepest layer of the encoder. This allows the network to eliminate unnecessary stimuli and focus more on important features.
In the experiments, we collected 979 X-ray images of electronic components with defects. These images were divided into a training set of 881 images and a test set of 98 images. During training, sample data were augmented into eightfold. The original HarDNet-MSEG model achieves the performance on the training set listing as mean intersection over union (MIoU) of 91.53%, recall of 95.21%, and precision of 95.69%. On the test set, it achieves MIoU of 78.23%, recall of 83.67%, and precision of 92.05%. After the proposed modification, the modified model′s performance is remarkably improved. On the training set, it achieved a MIoU of 95.27%, recall of 97.86%, and precision of 97.83%. On the test set, it achieved a MIoU of 86.56%, recall of 92.59%, and precision of 93.95%. | en_US |