基于SMT-YOLOv8的PCB缺陷检测研究
Research on PCB defect detection based on SMT-YOLOv8
王军 1伍毅 1陈正超1
作者信息
- 1. 江西理工大学机电工程学院 赣州 341000
- 折叠
摘要
针对PCB缺陷检测中目标尺寸小、权重文件庞大难以部署的问题,提出一种改进的YOLOv8小目标缺陷检测方法.该方法将SE注意力机制融入C2f中,使网络能够根据通道域的信息给图像不同位置赋予不同的权重,获取更重要的特征信息;在SPPF中引入Basic RFB以增强网络感受野,提升网络的特征提取能力;新增小目标检测尺度,提升模型对微小缺陷的检测能力;舍弃大目标检测尺度,降低计算负荷并缩小权重文件.实验结果表明,在公开的PCB缺陷数据集,改进后的YOLOv8较原算法平均精度提升了2.6％、权重文件缩小了27.3％、FPS达到34.4 ms/帧.
Abstract
Aiming at the problem of small target size and huge weight file difficult to deploy in PCB defect detection,an improved YOLOv8 small target defect detection method is proposed.The method incorporates the SE attention mechanism into C2f,which enables the network to assign different weights to different locations in the image based on the information in the channel domain to obtain more important feature information;introduces Basic RFB in SPPF to enhance the network sensing field and improve the feature extraction capability of the network;adds a new small target detection scale to improve the model's ability to detect tiny defects;discards the large target detection scale to reduce the computational load and shrink the weight file.The experimental results show that the improved YOLOv8 improves the average accuracy by 2.6％,shrinks the weight file by 27.3％,and achieves an FPS of 34.4 ms/frame over the original algorithm in the publicly available PCB defective dataset.
关键词
PCB/缺陷检测/YOLOv8/SE/Basic/RFB/小目标检测尺度Key words
PCB/defect detection/YOLOv8/SE/Basic RFB/small target detection scale引用本文复制引用
基金项目
江西省重大科技研发专项(20223AAG02019)
出版年
2024
NETL
NSTL
万方数据