Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling

Recycling steel scrap can reduce carbon dioxide (CO2) emissions from the steel industry. However, a significant challenge in steel scrap recycling is the inclusion of impurities other than steel. To address this issue, we propose vision-language-model-based anomaly detection where a model is finetuned in a supervised manner, enabling it to handle niche objects effectively. This model enables automated detection of anomalies at a fine-grained level within steel scrap. Specifically, we finetune the image encoder, equipped with multi-scale mechanism and text prompts aligned with both normal and anomaly images. The finetuning process trains these modules using a multiclass classification as the supervision.

@article{tanaka2025_2506.13282,
  title={ Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling },
  author={ Daichi Tanaka and Takumi Karasawa and Shu Takenouchi and Rei Kawakami },
  journal={arXiv preprint arXiv:2506.13282},
  year={ 2025 }
}