摘要
针对现有的印刷电路板(PCB)缺陷检测算法无法兼顾检测精度、模型参数量和计算量等问题,提出一种基于轻量化ADS-YOLOv8n的PCB缺陷检测算法。首先,引入ADown下采样模块以保留更多细节缺陷信息,增强对细节缺陷的提取能力;其次,设计了一种融合3层特征的DTFM模块,加强特征提取和对缺陷的定位能力;然后,设计了新的SCM模块,增强对缺陷信息的关注;最后,引入WIoUv3边界框损失函数,使模型获得更准确的回归结果。改进后的模型平均精度均值达到了98.43%,召回率达到了96.58%,相较于基准模型,平均精度均值提升了3.20百分点,召回率提升了5.17百分点,参数量和计算量分别减少了5.0×10^(5)和3.0×10^(8),在提升检测精度的基础上兼顾了模型轻量化。
In view of the issue of balancing detection accuracy with the number of parameters and computational load in printed circuit board(PCB)defect detection,this study proposes a lightweight PCB defect detection algorithm based on ADS-YOLOv8n.Firstly,the ADown downsampling module is introduced to retain more detailed defect information and enhance the ability to extract detail defects.Secondly,a DTFM module incorporating three layers features is designed to enhance feature extraction and ability to localize defects.Then,a new SCM module is designed to enhance the focus on defect information.Finally,the WIoUv3 bounding box loss function is introduced to enable the model to obtain more accurate regression results.The mean average precision of the improved model reaches 98.43%and the recall rate reaches 96.58%,compared with the benchmark model,the mean average precision is improved by 3.20 percentage points,the recall rate is improved by 5.17 percentage points,and the number of parameters and computation volume are reduced by 5.0×10^(5) and 3.0×10^(8),respectively.The improved model takes into account the lightweight of the model on the basis of improving the detection precision.
作者
胡琪涛
邹启杰
Hu Qitao;Zou Qijie(College of Information Engineering,Dalian University,Dalian 116622,Liaoning,China)
出处
《激光与光电子学进展》
北大核心
2025年第8期113-125,共13页
Laser & Optoelectronics Progress
基金
2021年辽宁省教育厅项目(LJKZ1180)。
