Deep Siamese Semantic Segmentation Network for PCB Welding Defect Detection

Deep learning has been widely used in recent years for printed circuit board (PCB) defect detection because of its excellent performance. However, deep-learning-based approaches often suffer from the over-fitting problem due to the lack of sufficient training data in real applications. Meanwhile, these approaches still have some challenges to detect these defects with small sizes and irregular shapes. To address these problems, this article has developed a novel deep Siamese semantic segmentation network which integrates the similarity measurement of the Siamese network with the encoder-decoder semantic segmentation network for PCB welding defect detection. This network includes two encoders sharing weighted values, a decoder, and some correlation modules, in which the decoder integrates deep features from two decoders with their feature difference computed by some correlation modules via skipping connections to recover spatial information on multiple output layers, and thus this proposed network can perform PCB welding small defect semantic segmentation. Moreover, via these correlation modules, this proposed network can pay more attention to semantic difference and further alleviate the over-fitting issue because of insufficient defect samples. Finally, we propose a combined loss function which combines the weighted cross-entropy loss, the Lovasz softmax loss, and the weighted precision-recall loss for network training to further improve small defect segmentation and recall improvement. Experimental results demonstrate that the proposed network can be trained on limited training images and achieve high efficiency and outstanding effects for PCB welding small defect segmentation.

Automated summary

This article proposes a novel deep Siamese semantic segmentation network for PCB welding defect detection, which combines the similarity measurement of the Siamese network with the encoder-decoder semantic segmentation network. The network effectively addresses the challenges faced by deep learning in PCB defect detection, achieving semantic segmentation of small defects.