贴片电感表面缺陷检测数据集

本数据集用于实现贴片电感的自动化缺陷检测，解决当前市面上贴片电感数据稀缺问题，通过构建该数据集，为后续贴片电感缺陷检测模型算法提供训练数据支撑。结合缺陷检测算法，能实时检测打反漏磁等致命缺陷，自动拦截不良品，减少人工复检的人力物力消耗。根据该真实生产过程收集的缺陷数据，建立缺陷-工艺参数关联模型，优化制作参数。

This dataset is developed for automated defect detection of chip inductors, addressing the critical issue of scarce available chip inductor data in the current market. Constructing this dataset provides training data support for subsequent defect detection model algorithms targeting chip inductors. When paired with defect detection algorithms, it enables real-time detection of fatal defects such as polarity-reversed installation and magnetic leakage, automatically rejects defective products, and reduces the labor and material costs incurred by manual re-inspection. Based on defect data collected from actual production processes, a correlation model between defects and process parameters can be established to optimize manufacturing parameters.

搜集汇总

背景与挑战

背景概述

该数据集收录SMT产线贴片电感表面缺陷图像，涵盖薄涂层、刮花、漏磁、缺角、异物、大异物、打反七类缺陷类型，通过多角度工业相机采集高像素JPG格式图像。数据集专为电子元器件自动化质检算法开发设计，支持语义分割模型训练以实现像素级缺陷定位，适用于提升AOI检测系统精度和优化生产工艺。