改进YOLOv8n的轻量级PCB缺陷检测算法OA北大核心CSTPCD
Lightweight PCB defect detection algorithm for improved YOLOv8n
针对当前印刷电路板(PCB)小目标缺陷检测精度低,现有检测模型庞大,在边缘端设备部署难的问题,提出一种基于改进YOLOv8n的PCB缺陷轻量化检测模型YOLOv8-t.首先使用FasterNet Block和Slim-Neck结构对Bockbone和Neck进行结构优化,减少冗余计算和内存访问,解决特征冗余导致的检测精度低的问题;其次使用自研检测头Detect_G提高模型检测的速度和精度;最后引入基于跨空间多尺度的注意力(EMA)机制提高对小目标…查看全部>>
In view of the low accuracy of small object defect detection in printed circuit boards(PCBs)and the fact that the existing detection models are large and difficult to be deployed in edge equipment,a lightweight PCB defect detection model YOLOv8-t is proposed based on the improved YOLOv8n.The structures of the FasterNet Block and the Slim-Neck are used to optimize the structures of the Bockbone and the Neck to reduce the redundant calculations and memory acce…查看全部>>
张淑卿;孟昊;葛超
华北理工大学 电气工程学院,河北 唐山 063210华北理工大学 电气工程学院,河北 唐山 063210华北理工大学 电气工程学院,河北 唐山 063210
电子信息工程
YOLOv8PCB轻量化缺陷检测注意力机制Detect_G
YOLOv8PCBlightweightdefect detectionattention mechanismDetect_G
《现代电子技术》 2024 (15)
115-121,7
河北省自然科学基金资助项目(F2021209006)
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