电商智能质检与企业信息化融合研究——基于两阶段深度分割模型的工业品应用

Research on the Integration of Intelligent Quality Inspection and Enterprise Informatization in E-Commerce—An Industrial Application of a Two-Stage Deep Segmentation Model

摘要: 在工业品电商和线上制造服务快速发展的背景下，商品图像既是用户感知质量的重要载体，也是平台开展入库质检、售后溯源和营销素材管理的关键数据来源。受限于仓储与生产环境的复杂性，商品表面常出现对比度低、边界模糊、尺度差异大的细微缺陷，传统依赖人工抽检和简单规则算法的质检方式在准确性、一致性和处理效率方面存在明显不足。为此，本文在显著性目标检测的基础上，构建了一种面向电商场景的深度学习智能质检方案：通过两阶段“粗检测–细优化”的显著性分割模型，对商品图像中的潜在瑕疵区域进行自动定位和轮廓提取，并结合平台业务流程，将检测结果结构化为可用于质量标记和风险预警的指标。基于某工业品电商合作企业采集的带钢表面图像数据开展实验表明，相比多种现有显著性检测与工业缺陷检测方法，该方案在边界刻画、小目标识别和噪声抑制等方面具有明显优势，并能在保证毫秒级推理速度的前提下有效降低漏检率。研究进一步从质量管理和平台治理视角分析了智能质检在入库筛查、上架审核和售后处理等环节的应用模式，指出在主数据统一和流程可追溯的企业信息化基础之上，引入深度显著性检测模型有助于构建“源头质检–线上展示–用户反馈”的质量闭环，为工业品电商高质量发展提供支撑。

Abstract: Against the backdrop of the rapid development of industrial e-commerce and online manufacturing services, product images have become not only the primary carrier of customers’ perceived quality, but also a key data source for inbound inspection, after-sales tracing and marketing asset management on e-commerce platforms. Owing to the complexity of warehousing and production environments, subtle surface defects on products—such as low-contrast flaws with blurred boundaries and diverse scales—occur frequently, making traditional quality inspection methods that rely on manual sampling and simple rule-based algorithms inadequate in terms of accuracy, consistency and processing efficiency. To address these issues, this paper proposes a deep-learning-based intelligent inspection scheme tailored to e-commerce scenarios, built on saliency-based target detection. A two-stage coarse-to-fine saliency segmentation model is employed to automatically locate potential defect regions in product images and extract their contours. The detection results are then converted into structured indicators that can be used for quality labeling and risk warning, in line with platform business processes. Experiments conducted on strip-steel surface images collected from an industrial e-commerce partner show that, compared with several existing saliency detection and industrial defect detection methods, the proposed scheme has clear advantages in boundary delineation, small-target recognition and noise suppression, while achieving millisecond-level inference speed and effectively reducing the miss-detection rate. From the perspectives of quality management and platform governance, this study further analyzes application patterns of intelligent inspection in inbound screening, listing review and after-sales handling. The analysis suggests that, on the basis of unified master data and traceable business processes enabled by enterprise informatization, introducing deep saliency detection models helps build a closed quality loop of “source inspection - online presentation - user feedback”, thereby supporting the high-quality development of industrial e-commerce.