电池盖板表面缺陷检测数据集

通过工业相机采集电池盖板表面图像，利用语义分割模型实现对毛刺、模印、生锈、压伤划痕、脏污5类缺陷图像自动化识别分类。本缺陷检测数据具有以下应用场景：1. 通过实时采集模具表面缺陷数据，构建注塑工艺参数与缺陷类型的关联模型，实现工艺动态调整，降低生产废品率；2. 积累的缺陷样本数据库，为缺陷检测模型训练提供数据支撑。

Surface images of battery cover plates are acquired using industrial cameras, and a semantic segmentation model is utilized to automate the recognition and classification of five defect categories: burrs, mold imprints, rust, press-induced scratches, and contaminations. This defect detection dataset has the following application scenarios: 1. By collecting mold surface defect data in real time, a correlation model between injection molding process parameters and defect types can be constructed to realize dynamic adjustment of production processes and reduce the production scrap rate; 2. The accumulated defect sample database provides robust data support for the training of defect detection models.

搜集汇总

背景与挑战

背景概述

该数据集包含电池盖板表面缺陷的高分辨率图像，涵盖毛刺、模印、生锈、压伤划痕和脏污五类常见缺陷类型，采用工业相机和特定光源方案采集。数据集主要用于工业自动化缺陷检测和语义分割模型训练，以提升电池制造过程中的质量管控效率和精度。