【摘要】 目前基于传统的机器视觉分析方法筛选后的PCB焊接缺陷图像还需要进行人工的复检流程,工作量大导致视觉疲劳后容易出错.为了改善这种现状,本文设计应用YOLOv3-spp的目标检测算法来构建焊接缺陷检测模型.为提升检测速度,采用模型剪枝、模型蒸馏、模型量化等技术对检测模型进行压缩优化,采用深度学习加速组件OpenVINO来加载压缩优化后的检测模型,实现对PCB焊接缺陷图像的复检.基于该优化算法设计了一种基于深度学习技术的PCB焊接缺陷检测识别系统.它能快速、准确地识别焊接缺陷并定位缺陷位置,解决了人工目检带来的效率低下、漏检误检率高等问题.更多还原

【Abstract】 At present, PCB welding defect images screened with traditional machine vision analysis methods still need manual reinspection, which is easy to make mistakes after visual fatigue due to heavy workload. In view of this, the study designs and applies the YOLOv3-spp object detection algorithm to build a welding defect detection model. For a higher detection speed, model pruning, model distillation, model quantization and other technologies are used to compress and optimize the detection model. OpenVINO, a deep learning acceleration component, is employed to load the compressed and optimized detection model for the reinspection of PCB welding defect images. With the help of this optimization algorithm, this study designs a PCB welding defect detection and identification system based on deep learning technology.It can quickly and accurately identify welding defects and locate the defects, addressing the low efficiency and high rates of missed detection and false detection caused by manual visual inspection.更多还原