{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T14:51:27Z","timestamp":1772635887028,"version":"3.50.1"},"reference-count":62,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Program of Qilu Institute of Technology","award":["QIT24NN036, QIT24TP029"],"award-info":[{"award-number":["QIT24NN036, QIT24TP029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Accurate and efficient surface defect detection is crucial for ensuring the structural integrity and quality of industrial steel products. However, existing detectors still struggle with sub-pixel, low-contrast defects and extreme variations in defect scale and shape. To address these challenges, we propose TriA-Mamba-You Only Look Once (YOLO), a triple-awareness enhancement of the Mamba-YOLO framework for steel defect detection. First, the input samples are processed using a discrete wavelet transform, which reduces background noise and enhances the representation of defect features. Next, the backbone is enhanced with a micro-feature-aware enhancement block, which comprises a Smooth-Mix Focus Stem (FS) block to minimize aliasing artifacts while preserving critical defect features, combined with a Selective Fusion Attention (SF-Att) block for refinement by selectively emphasizing discriminative micro-scale patterns. Within the neck, a context-aware feature enhancement module is implemented to tackle extreme scale and shape variance. It utilizes a receptive-field lightweight spatio-channel feature refinement (RLSC-FR) block to optimize local structure and receptive field characteristics, and an Attention-Augmented Visual State-Space (AA-VSS) block to dynamically adapt to defect scale and morphological variations. Finally, a path-importance-aware fusion module, using Norm-Weighted concatenation (NW-Concat), is developed for multi-path feature fusion, dynamically adjusting contribution weights to ensure balanced aggregation. Experimental results show that TriA-Mamba-YOLO achieves a mean average precision (mAP) of 81.3%, surpassing the original Mamba YOLO by 5.2% on the Northeast University-Defect Detection (NEU-DET) dataset, and achieves a balance between accuracy, computational efficiency, and robustness. These results highlight the effectiveness of the proposed approach for industrial surface defect detection applications.<\/jats:p>","DOI":"10.7717\/peerj-cs.3626","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:18:50Z","timestamp":1772612330000},"page":"e3626","source":"Crossref","is-referenced-by-count":0,"title":["Boosting Mamba-You Only Look Once (YOLO) with triple-aware enhancement for steel surface defect detection"],"prefix":"10.7717","volume":"12","author":[{"given":"Xue","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Computer and Information Engineering, Qilu Institute of Technology, Jinan, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5854-0788","authenticated-orcid":true,"given":"Delanyo Kwame Bensah","family":"Kulevome","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Engineering, Qilu Institute of 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