一种自适应目标区域提取的小样本丝印字符缺陷检测方法

doi:10.3969/j.issn.1004-132X.2025.12.020

摘要/Abstract

摘要：

针对因电子器件丝印字符材质反光引起分割精度差、检测图像位姿不确定难以自适应定位提取目标区域、缺陷检测目标小及样本少导致识别准确率低的问题，提出自适应目标区域提取的小样本丝印字符缺陷检测方法。设计基于大津阈值法的自适应二次阈值分割算法，减少反光材质表面光亮目标区域信息损失，实现光照不均匀图像的准确分割；提出自适应目标区域定位提取与角度矫正算法，解决因字符大小及位姿变化难以实现自适应目标区域的精确定位与提取问题；研究了缺陷检测目标初识别、置信度低小目标字符二次精识别方法，实现了小目标、少样本缺陷检测目标的准确识别。实验结果表明：二次阈值分割算法能够实现光照不均字符图像的准确分割，自适应目标区域定位与角度矫正准确率达99.5%，轻量化深度学习模型的字符分类识别率达99.1%，字符缺陷检测准确率达98.6%，检测速度为0.083 s/张，满足工业生产中在线检测精度与速度的需求。

关键词: 缺陷检测, 丝网印刷, 光照不均匀, 位姿不确定, 小样本识别

Abstract:

The defect detection of screen-printed characters on electronic devices faced problems such as poor segmentation accuracy caused by reflective materials， difficulty in adaptively locating and extracting target regions due to uncertain image poses， and low recognition accuracy caused by small defect detection targets and insufficient samples， an adaptive target region extraction method for few-shot screen-printed character defect detection was proposed. Based on OTSU method an adaptive dual-threshold segmentation algorithm was designed to reduce information loss in bright target regions on reflective material surfaces， achieving accurate segmentation of images with uneven illumination. An adaptive target region localization， extraction， and angle correction algorithm was proposed to solve the problems of precisely locating and extracting adaptive target regions despite variations in character sizes and poses. A method involving preliminary defect target recognition and secondary fine recognition for low-confidence small target characters was studied， achieving accurate recognition for small-target， few-sample defect detection targets. Experimental results demonstrate that： the dual-threshold segmentation algorithm achieves accurate segmentation of character images under uneven illumination； the accuracy of adaptive target region localization and angle correction reaches 99.5%； the character classification recognition rate of the lightweight deep learning model reaches 99.1%； the character defect detection accuracy reaches 98.6%； and the detection speed is as 0.083 seconds per image. These results meet the requirements for both of precision and speed in industrial online detections.

Key words: defect detection, screen printing, non-uniform lighting, pose variation, small sample recognition

中图分类号:

TP277

胡新宇, 张骏巍, 艾佳, 李云翎, 严爽. 一种自适应目标区域提取的小样本丝印字符缺陷检测方法[J]. 中国机械工程, 2025, 36(12): 2968-2977.

Xinyu HU, Junwei ZHANG, Jia AI, Yunling LI, Shuang YAN. A Small-sample Defect Detection Method for Screen-printed Characters with Adaptive Target Region Extraction[J]. China Mechanical Engineering, 2025, 36(12): 2968-2977.

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链接本文: https://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2025.12.020

https://www.cmemo.org.cn/CN/Y2025/V36/I12/2968

图/表 25

图1 视觉识别系统

Fig.1 Visual identity system

图2 算法流程图

Fig.2 Algorithm flowchart

图3 平板电脑样品

Fig.3 The physical platform of the system

图4 部分缺陷图

Fig.4 Partial defect diagram

图5 系统实物平台

Fig.5 The physical platform of the system

图6 改进算法二值化

Fig.6 The binarization of the improved algorithms

图7 二值化流程图

Fig.7 Binary flowchart

图8 目标定位

Fig.8 Target positioning

图9 角度示意图

Fig.9 Angular diagram

图10 角度纠正图

Fig.10 Angular correction diagram

图11 y方向像素点统计

Fig.11 Y-direction pixel statistics

图12 分割结果

Fig.12 Result of segmentation

图13 SE模块结构

Fig.13 SE module structure

图14 ECA模块结构

Fig.14 ECA module structure

图15 YOLOv5s-MEF模型结构

Fig.15 Structure of the YOLOv5s-MEF model

图16 模型识别

Fig.16 Model recognition

图17 工作界面

Fig17 Work interface

图18 增强图

Fig.18 Enhanced images

表1 不同改进策略下的实验模型

Tab.1 Experimental models under different improvement strategies

	组2	组3	组4	组5	组6
轻量化	√	√	√		√
ECA		√			√
损失函数改进			√	√	√

表2 消融实验结果

Tab.2 Ablation experiment results

实验组序	准确率/%	召回率/%	FPS	F1/%
1	97.7	89.2	54.64	93.25
2	96.8	88.3	65.79	92.35
3	98.2	90.1	66.23	93.97
4	97.8	90.0	61.12	93.73
5	98.3	90.3	51.62	93.98
6	99.1	92.2	62.11	95.52

图19 部分处理结果图

Fig.19 Graph of partial processing results

图20 缺陷识别结果

Fig.20 Defect identification result

表3 算法对比结果

Tab.3 The result of algorithm comparisons

方法	检测时间/s	准确率/%
改进YOLOv5s模型	0.021	73
本文算法	0.083	99

图21 不同方法的识别结果对比

Fig.21 Comparison of recognition results of different methods

表4 三种算法检测性能

Tab.4 Performance comparison of three detection algorithms

方法	检测时间/s	准确率/%
改进模板匹配法	0.286	97.7
改进SSD算法	0.052	95.5
本文算法	0.083	98.6

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