YOLOv8-DEE: a high-precision model for printed circuit board defect detection.

ABSTRACT: Defects in printed circuit boards (PCBs) occurring during the production process of consumer electronic products can have a substantial impact on product quality, compromising both stability and reliability. Despite considerable efforts in PCB defect inspection, current detection models struggle with accuracy due to complex backgrounds and multi-scale characteristics of PCB defects. This article introduces a novel network, YOLOv8-DSC-EMA-EIoU (YOLOv8-DEE), to address these challenges by enhancing the YOLOv8-L model. Firstly, an improved backbone network incorporating depthwise separable convolution (DSC) modules is designed to enhance the network's ability to extract PCB defect features. Secondly, an efficient multi-scale attention (EMA) module is introduced in the network's neck to improv

SUBMITTER: Yi F 

PROVIDER: S-EPMC11888845 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature