用视觉技术自动估算废钢杂质含量，提升钢铁生产效率与安全
From Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metal
- 通过多实例学习和多任务学习，从卸车图像预测杂质百分比
- 杂质估计误差仅0.27（MAE），分类准确率F1达0.79
- 系统支持实时推理，适合钢铁厂质检与熔炼规划场景
废钢质量直接影响钢铁冶炼中的能耗、排放与安全。当前杂质含量主要依赖人工目视判断，主观性强且存在粉尘与移动设备带来的安全隐患。本文提出一种辅助计算机视觉流程，基于卸车过程中拍摄的图像，估算杂质比例（按百分比计）并分类废钢类型。方法将杂质评估建模为车厢级别的回归任务，利用多实例学习（MIL）和多任务学习（MTL）处理序列数据。最佳结果为MIL模型实现MAE 0.27、R² 0.83；MTL模型在废钢分类上达到MAE 0.36、F1 0.79。系统已实现在验收流程中的近实时运行：磁铁/车厢检测划分时间层，版本化推理服务输出车厢级估计与置信度，操作员可结构化修正，不确定案例反馈至主动学习循环以持续优化。该流程降低人为偏差，提升作业安全，并可嵌入验收与熔炼计划工作流。
原文摘要 · Abstract (English)
Scrap quality directly affects energy use, emissions, and safety in steelmaking. Today, the share of non-metallic inclusions (contamination) is judged visually by inspectors - an approach that is subjective and hazardous due to dust and moving machinery. We present an assistive computer vision pipeline that estimates contamination (per percent) from images captured during railcar unloading and also classifies scrap type. The method formulates contamination assessment as a regression task at the railcar level and leverages sequential data through multi-instance learning (MIL) and multi-task learning (MTL). Best results include MAE 0.27 and R2 0.83 by MIL; and an MTL setup reaches MAE 0.36 with F1 0.79 for scrap class. Also we present the system in near real time within the acceptance workflow: magnet/railcar detection segments temporal layers, a versioned inference service produces railcar-level estimates with confidence scores, and results are reviewed by operators with structured overrides; corrections and uncertain cases feed an active-learning loop for continual improvement. The pipeline reduces subjective variability, improves human safety, and enables integration into acceptance and melt-planning workflows.
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