Segmentation of Printed Circuit Board Components Based on Nested U-shape Structures
As an important modern electronic component,component segmentation of highly integrated Printed Circuit Boards(PCB)is a typical small-object image segmentation task.Owing to the excessive introduction of complex backgrounds,component segmentation of PCB images faces challenges such as insufficient boundary-awareness capability.To improve the boundary-awareness capability,an externally coordinated nested U-shape network structure called U2ECNet is proposed.Specifically,the backbone network of the algorithm is a nested U-shape structure,and an external expansion module is used in the coding and decoding system to efficiently learn global and local information and focus on the edge and corner details in the component region.A bootstrap refinement module is used to optimize the segmentation accuracy of the model and improve the segmentation effect of PCB components by aggregating the global semantic information through multi-scale feature mapping.PCB_SOD,a new image segmentation dataset containing 5 608 training images and 2 403 test images,was used to perform the segmentation task and trained in the proposed network.Experiments on the DUTS and PCB_SOD datasets show that the network model achieved a Mean Absolute Error(MAE)of 0.045 and 0.027 and max Fβ of 86.1％and 87.2％,respectively,demonstrating reduced MAE and improved max Fβ,achieving the best overall segmentation performance compared to other methods.The proposed externally coordinated nested U-shape structure improves the accuracy of PCB component segmentation,exhibits good robustness in complex backgrounds,and generates the most accurate saliency segmentation graph.
万方数据
维普
