Wei, Changyun, Bao, Yuhang, Zheng, Chengwei and Ji, Ze  ORCID: https://orcid.org/0000-0002-8968-9902
2025.
AMFNet: aggregated multi-level feature interaction fusion network for defect detection on steel surfaces.
Journal of Intelligent Manufacturing
10.1007/s10845-025-02613-5
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Abstract
Current models for detecting defects on steel surfaces struggle to fully utilize potential positional and semantic information. Usually, these models merely combine high-level and low-level features in a straightforward manner, leading to an increase in redundant information. To address this challenge, this study presents an aggregated multi-level feature interaction fusion network (AMFNet). Specifically, the AMFNet incorporates a branch interaction module (BIM) that branches and fuses features channel-wise to facilitate feature interaction. Moreover, it also includes a dilated context module (DCM) that expands the receptive field to capture contextual information across various scales effectively. In addition, we propose a spatial correlation module (SCM) that is designed to recognize spatial dependencies between adjacent feature maps and generate attention weights. Our performance evaluations on the NEU-DET and GC10-DET dataset reveal that our proposed AMFNet significantly outperforms classical object detectors in terms of mean average precision (mAP). Moreover, it also demonstrates a modest improvement over the advanced methods recently introduced by other researchers.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Publisher: | Springer |
| ISSN: | 0956-5515 |
| Funders: | National Natural Science Foundation of China |
| Date of First Compliant Deposit: | 13 May 2025 |
| Date of Acceptance: | 15 April 2025 |
| Last Modified: | 27 May 2025 10:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/178250 |
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