{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T09:04:15Z","timestamp":1790154255904,"version":"4.0.1"},"reference-count":33,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009967","name":"\u201cResearch and Application of an IoT-Based Intelligent Yard Management Platform for Logistics-Port Containers\u201d","doi-asserted-by":"publisher","award":["2024AB062"],"award-info":[{"award-number":["2024AB062"]}],"id":[{"id":"10.13039\/501100009967","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automatic container-number recognition in land-port yards is challenged by clutter, illumination changes, scale variation, surface wear, and character distortion. This paper proposes a two-stage detection\u2013cropping\u2013recognition framework comprising an improved YOLO11 detector and a lightweight convolutional recurrent neural network (CRNN). The detector integrates C3k2 with channel-split multiscale aggregation (C3k2-CSMA), a hierarchical efficient feature fusion pyramid network (HEFFPN), selective boundary aggregation (SBA), and a shared enhanced detection head (SED-Head) with detail-enhanced convolution (DEConv) to improve small-target representation, feature fusion, and boundary localization. The recognizer combines MobileNetV3 with bidirectional long short-term memory (Bi-LSTM) and is trained using connectionist temporal classification (CTC) loss and L2 regularization. The detector achieved 97.3% mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5), 97.1% recall, 55 frames per second (FPS), and a model size of 12.2 MB. The recognizer achieved 97.6% exact-match accuracy, a model size of 6.2 MB, and an inference time of 8.9 ms\/crop. The complete system achieved 96.8% end-to-end exact-match accuracy, a total model size of 18.4 MB, and a total inference time of 27.1 ms\/image, demonstrating a practical accuracy\u2013efficiency balance for the evaluated complex land-port scenes.<\/jats:p>","DOI":"10.3390\/s26196014","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:14:39Z","timestamp":1790151279000},"page":"6014","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Container-Number Detection and Recognition in Land-Port Yard Scenes Based on an Improved YOLO11 Detector and a Lightweight CRNN Recognizer"],"prefix":"10.3390","volume":"26","author":[{"given":"Yuan","family":"Wu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Yongman","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Yating","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Huibing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Jingqi","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Li","family":"Han","sequence":"additional","affiliation":[{"name":"Xinjiang Huiding Logistics Co., Ltd., Shihezi 832000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","unstructured":"(2022). 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