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基于机器视觉的板卡点胶缺失检测系统设计

Design of Detection System for PCB Board Dispensing Missing Based on Machine Vision
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摘要 采用人工目检方式进行贴装器件的加固点胶缺失检测存在人力成本高、效率低等问题。提出一种基于机器视觉的点胶缺失检测方法。使用色调饱和度值(HSV)阈值分割法识别板卡图像,利用最小包络矩形提取目标,使用霍夫变换及透视变换获取点胶区域的正视图像,标注分割出各个点胶块区域,识别每个点胶块的面积并保存为标准模板。对待检产品的图像采用上述方法进行处理,并与标准模板对比,在判别待检产品质量的同时定位点胶缺失的位置。采用基于机器视觉检测产品的方法进行实际的确认测试。结果表明,该方法能够准确、可靠地检测判别产品的点胶质量,并标识出所有的点胶缺失位置,其检测不合格品的准确率为100%,部分合格板卡有过检现象,检测的综合准确率为98.5%。该方法具有良好的准确性及可靠性,能够满足检测需求。 The manual visual inspection method is used to detect the lack of reinforcement dispensing of mounting devices,which has the problems of high labor cost and low efficiency.A method of missing dispensing detection based on machine vision is proposed.The hue-saturation-value(HSV)threshold segmentation method is used to identify the board image.The minimum envelope rectangle is used to extract the target image.The Hough transform and perspective transform are used to obtain the front-view image of the glue dispensing area.Each glue dispensing area is manually marked and segmented,and the area of each glue dispensing is recognized and saved as a standard template.The image of the product to be inspected is processed by the above method,and compared with the standard template,the quality of the product to be inspected is judged and the location of the missing glue position is identified.The method based on machine vision is used to carry out the actual confirmation test.The results show that the method can accurately and reliably detect and judge the dispensing quality of the product,and identify all dispensing missing locations.The accuracy rate of detecting unqualified products is 100%,and some qualified boards have passed the inspection.The comprehensive accuracy rate of detection is 98.5%.It has good accuracy and reliability,and can meet the detection requirements.
作者 丁涛杰 席浩洋 潘晗 倪云龙 孟祥冬 DING Taojie;XI Haoyang;PAN Han;NI Yunlong;MENG Xiangdong(China Electronics Technology Group Corporation No.58 Research Institute,Wuxi 214035,China;School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China)
出处 《电子与封装》 2023年第5期20-26,共7页 Electronics & Packaging
基金 辽宁省科技厅联合开放基金机器人学国家重点实验室开放基金资助项目(2021KF2205) 中国博士后科学基金面上资助项目(2022M712976) 江苏省自然科学青年基金项目(BK20210070)。
关键词 机器视觉 霍夫变换 透视变换 标准模板 machine vision Hough transform perspective transform standard template
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