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arXiv · 2506.13282

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

Daichi Tanaka·Takumi Karasawa·Shu Takenouchi·Rei Kawakami
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Abstract

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.

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Daichi Tanaka, Takumi Karasawa, Shu Takenouchi, Rei Kawakami. 2025-06-16. Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling. https://arxiv.org/abs/2506.13282

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