AI Cuts the Cost of Labeling Scrap Steel Images with Pseudo-Labels

Sorting scrap steel is a messy computer vision problem. Piles of discarded metal are densely stacked, individual pieces differ in fine-grained shape, and some categories appear far more often than others, so building a reliable detector has traditionally required enormous amounts of hand-labeled imagery. Even carefully annotated datasets tend to miss objects or mark them imprecisely, and fully manual labeling is prohibitively labor-intensive. A new study published in Scientific Reports tackles this annotation bottleneck with a semi-supervised pipeline that lets a machine do most of the labeling work.

The researchers, led by Shaolun Sun and colleagues, call their system SSPL-YOLO11, a Semi-Supervised Pseudo-Label YOLO11 detector. It is built on an offline teacher–student pseudo-labeling framework: a trained teacher model generates candidate labels for unlabeled images, and a student model learns from them. The key innovation is a three-level quality control mechanism applied to those machine-generated pseudo-labels. First, confidence thresholding filters out detections the model is unsure about. Second, a deduplication step using Distance-IoU non-maximum suppression, or DIoU-NMS, removes overlapping duplicate boxes, which is critical when objects are tightly stacked. Third, a lightweight manual verification pass lets human annotators correct only the small fraction of labels that survive the automated filters.

The team ran controlled experiments to isolate the effects of each design choice, quantifying how the confidence threshold, the deduplication strategy, and the initialization from pre-trained weights each influenced performance. On the public Scrap Steel Image Dataset (SSID), SSPL-YOLO11 reached 88.25% mAP50 and 65.58% mAP50–95 on an independent test set. That outperforms a fully supervised YOLO11 baseline by 3.92 and 7.74 percentage points respectively, meaning the semi-automatic pipeline did not merely save labor but actually produced a more accurate detector than conventional manual annotation on this benchmark.

The authors attribute the gains to the pseudo-label quality control recovering unlabeled objects that manual annotation had missed, at only a small manual cost. The researchers position the approach as a practical route toward intelligent scrap steel sorting and recycling, where automated recognition of metal categories could support more efficient material recovery. As with any single-dataset study, broader validation on other industrial imagery would be needed to confirm how general the workflow is, but the reported results suggest that carefully filtered machine labels can beat fully hand-drawn ones in visually cluttered, imbalanced domains.

Subject of Research: Semi-supervised pseudo-labeling with DIoU-NMS for scrap steel object detection

Article Title: Pseudo-label optimization and DIoU-NMS for semi-automatic annotation in scrap steel detection

Article References: Sun, S., Chen, J., Ding, Y., Geng, M., Pan, H., Li, H., Li, Z., & Yin, W. (2026). Pseudo-label optimization and DIoU-NMS for semi-automatic annotation in scrap steel detection. Scientific Reports. https://doi.org/10.1038/s41598-026-76096-6

Image Credits: AI Generated

DOI: 10.1038/s41598-026-76096-6

Keywords: scrap steel detection, pseudo-labels, semi-supervised learning, YOLO11, DIoU-NMS, computer vision, object detection, annotation, recycling, deep learning, Pseudo-label, optimization