{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T10:38:19Z","timestamp":1791974299949,"version":"4.4.0"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Reliable detection of engraved surface codes on surgical instruments is essential for end-to-end traceability, yet remains challenging in practice because metallic reflection, motion blur, scale variation and weak textures often hinder stable localization. Here we present LA-DBNet, a lightweight detection framework built on DBNet for this task. The model uses MobileNetV4 with LiteFPN to reduce complexity while preserving multi-scale feature representations. To better capture the elongated structure and edge features of engraved codes, we introduce a Directional Edge Collaborative Alignment (DECA) module to improve cross-scale feature alignment, and embed an Efficient Channel Attention (ECA) mechanism in the high-resolution feature layer to enhance responses relevant to the target and suppress noise caused by reflections. We further incorporate a region-weighted consistency learning strategy during training to improve robustness to degraded samples. On our surgical instrument code dataset, LA-DBNet achieves an F1 of 95.8%, improving DBNet by 3.6 percentage points, while reducing parameters to 3.35 M and reaching 33.6 FPS. On ICDAR2015, it attains an F1 of 86.1%. These results show that LA-DBNet improves detection performance while substantially reducing model size and maintaining efficient inference in surgical instrument code detection.<\/jats:p>","DOI":"10.1371\/journal.pone.0355611","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:36:07Z","timestamp":1786124167000},"page":"e0355611","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["A lightweight alignment-aware DBNet for surgical instrument code detection"],"prefix":"10.1371","volume":"21","author":[{"given":"Ke","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuchang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8294-1999","authenticated-orcid":true,"given":"Zhifeng","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"pone.0355611.ref001","article-title":"Effects of implementing a barcode information management system on operating room staff: comparative study","volume":"26","author":"C-Y Li","year":"2024","journal-title":"J Med Internet Res"},{"issue":"6","key":"pone.0355611.ref002","doi-asserted-by":"crossref","DOI":"10.1002\/aorn.14145","article-title":"Implementing a barcode-based whole-life cycle management process to improve robotic instrument tracking","volume":"119","author":"C Qian","year":"2024","journal-title":"AORN J"},{"key":"pone.0355611.ref003","article-title":"Smart management of single surgical instruments through unique device identification (UDI) barcode tracking system for enhancing sterilization quality and patient safety","volume":"5","author":"JH Liu","year":"2025","journal-title":"Antimicrob Steward Healthc Epidemiol"},{"key":"pone.0355611.ref004","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.jss.2024.09.087","article-title":"Comparison of reading times of RFID-tagged and barcode-engraved surgical instruments","volume":"304","author":"K Kusuda","year":"2024","journal-title":"J Surg Res"},{"issue":"5","key":"pone.0355611.ref005","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s10462-020-09930-6","article-title":"Deep learning approaches to scene text detection: a comprehensive review","volume":"54","author":"T Khan","year":"2021","journal-title":"Artif Intell Rev"},{"key":"pone.0355611.ref006","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/978-3-319-46484-8_4","article-title":"Detecting Text in Natural Image with Connectionist Text Proposal Network.","author":"Z Tian","year":"2016","journal-title":"Computer Vision - ECCV 2016. 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