Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915346517065728 |
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| author | Tanaka, Daichi Karasawa, Takumi Takenouchi, Shu Kawakami, Rei |
| author_facet | Tanaka, Daichi Karasawa, Takumi Takenouchi, Shu Kawakami, Rei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling Tanaka, Daichi Karasawa, Takumi Takenouchi, Shu Kawakami, Rei Computer Vision and Pattern Recognition 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. |
| title | Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.13282 |