NEU-CLS

The NEU-CLS dataset is collected from real industrial production lines. It is publicly released by the Surface Inspection Laboratory of Northeastern University, constructed explicitly for strip surface defect classification. This dataset covers six common types of strip surface defects: Crazing, Scratch, Pitted Surface, Patch, Rolled-in Scale, and Inclusion. The image's defect morphologies are diverse, presenting high complexity and diversity, effectively simulating the challenges of defect detection in practical application scenarios. Each defect category contains 300 images, all of which are grayscale with a resolution of 200×200 pixels.

NEU-CLS数据集采集自真实工业生产线，由东北大学表面检测实验室公开发布，专为带钢表面缺陷分类任务构建。该数据集涵盖六种常见带钢表面缺陷类型：裂纹（Crazing）、划痕（Scratch）、麻点表面（Pitted Surface）、斑块（Patch）、轧入氧化皮（Rolled-in Scale）与夹杂物（Inclusion）。图像中的缺陷形貌丰富多样，呈现出高度的复杂性与多样性，可有效模拟实际应用场景中缺陷检测所面临的各类挑战。每个缺陷类别均包含300张图像，所有图像均为分辨率200×200像素的灰度图像。

搜集汇总

背景与挑战

背景概述

NEU-CLS是一个用于带钢表面缺陷分类的工业图像数据集，由东北大学表面检测实验室从真实生产线收集并公开。它包含六种常见缺陷类型（如Crazing、Scratch等），每种缺陷提供300张200×200像素的灰度图像，缺陷形态多样且复杂，有效模拟了实际工业检测场景的挑战。