钢管外表面各类缺陷的实时检测识别数据

该数据基于机器视觉识别技术，对φ60mm以内钢管外表面各类缺陷进行实时检测、识别和分类判定；与传统目测方式相比更加准确地判断钢管缺陷，保证钢管质量，避免使用时因钢管质量瑕疵而出现的安全隐患；根据缺陷的检验结果，可以进一步的优化和改进钢管产品的研发，也是消费者在产品选型与购买时重要参考依据。1、数据采集来源：进行图像数据采集，通过工业相机和镜头采集图像数据，利用SDK接口获取图像帧信息，将数据信息进行清洗和筛选，避免极端误差信息影响分析；2、数据处理：利用图像预处理形成图像特征数据，训练样本数据，通过特征识别算法进行训练，形成特征样本，生成智能样本数据模型，将凹坑，裂纹，划伤，折叠数量进行多层次数据训练，使模型更加精确，利用神经网络和SVM算法来进行缺陷识别、判定和分类，缺陷分类判定准确率依托数据，进行长期持续性记录，从而反哺数据模型。

This dataset applies machine vision recognition technology to conduct real-time detection, recognition and classification of various surface defects of steel pipes with a diameter of 60 mm or less. Compared with traditional visual inspection, it enables more accurate judgment of steel pipe defects, ensures the quality of steel pipes, and eliminates potential safety hazards caused by substandard steel pipes during application. The defect inspection results can further optimize and improve the R&D of steel pipe products, and also provide an important reference for consumers when selecting and purchasing such products.

1. Data Collection Source: Image data is collected using industrial cameras and lenses. Image frame information is acquired via SDK interfaces, followed by data cleaning and filtering to remove extreme error data that could compromise analysis results.
2. Data Processing: Image preprocessing is conducted to generate image feature data for training sample datasets. Feature recognition algorithms are used for model training to produce feature samples and build an intelligent sample data model. Multi-level data training is performed on four common defects including pits, cracks, scratches and folding defects to enhance model accuracy. Neural networks and SVM algorithms are employed to implement defect detection, judgment and classification. The accuracy of defect classification and judgment is recorded over long-term continuous periods, and the accumulated data is fed back to continuously optimize the data model.

搜集汇总

数据集介绍

特点

该数据集包含624条钢管外表面缺陷的实时检测识别数据，采用机器视觉技术进行缺陷分类判定，准确率达97.10%，适用于钢管质量检测和产品研发优化。