Cracked & Intact Smartphone Images Dataset

The Cracked & Intact Smartphone Images Dataset is a collection of labeled images designed for training and evaluating machine learning models in smartphone screen crack detection. It contains over 1,000 high-resolution images, systematically categorized into two classes: cracked screens and intact screens. The images are captured under various lighting conditions and angles to enhance model robustness and generalization. The dataset is particularly useful for applications in quality control, automated damage assessment, and smartphone refurbishment processes. It supports tasks such as binary classification, object detection, and semantic segmentation. Each image is annotated to indicate the presence and location of cracks, facilitating supervised learning approaches. Available on Kaggle, this resource is openly accessible to researchers, developers, and enthusiasts working on computer vision and AI-driven diagnostic solutions. Its structured format and clear labeling make it suitable for both academic and industrial use, promoting advancements in automated visual inspection technologies.

带裂缝与完好屏幕的智能手机图像数据集（Cracked & Intact Smartphone Images Dataset）是一组带标注的图像集合，旨在为智能手机屏幕裂缝检测场景下的机器学习模型训练与评估提供支撑。该数据集包含超过1000张高分辨率图像，被系统性划分为两类：裂缝屏幕与完好屏幕。所有图像均在多种光照条件与拍摄角度下采集，以提升模型的鲁棒性与泛化能力。该数据集尤其适用于质量管控、自动化损伤评估以及智能手机翻新等流程中的应用场景，可支持二元分类、目标检测以及语义分割等多项计算机视觉任务。每张图像均带有标注，用以指示裂缝的存在与否及具体位置，可助力监督学习方法的落地应用。该数据集可在Kaggle平台获取，面向所有从事计算机视觉与AI驱动诊断方案研发的科研人员、开发者及爱好者开放共享。其结构化的存储格式与清晰的标注规范，兼顾学术研究与工业落地场景，能够推动自动化视觉检测技术的迭代升级。