{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T09:40:38Z","timestamp":1789465238613,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003695","name":"Korea Institute of Industrial Technology","doi-asserted-by":"publisher","award":["EH-26-0002"],"award-info":[{"award-number":["EH-26-0002"]}],"id":[{"id":"10.13039\/501100003695","id-type":"DOI","asserted-by":"publisher"}]},{"award":["EH-26-0002"],"award-info":[{"award-number":["EH-26-0002"]}],"id":[{"id":"https:\/\/ror.org\/04qfph657","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003662","name":"Korea Evaluation Institute of Industrial Technology","doi-asserted-by":"publisher","award":["RS-2024-00423589"],"award-info":[{"award-number":["RS-2024-00423589"]}],"id":[{"id":"10.13039\/501100003662","id-type":"DOI","asserted-by":"publisher"}]},{"award":["RS-2024-00423589"],"award-info":[{"award-number":["RS-2024-00423589"]}],"id":[{"id":"https:\/\/ror.org\/03z9cwa38","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Development of Energy Technology Program","award":["RS-2025-02314475"],"award-info":[{"award-number":["RS-2025-02314475"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Estimating metric depth for safety-relevant targets from a top-view crane camera is challenging because monocular predictions are scale-ambiguous and target regions of interest (RoIs) mix returns from payloads, personnel, hoisting wires, and the ground. We present a target-focused pipeline in which a detector defines RoIs, a dense metric-depth map is predicted, and one representative depth is extracted per object. A DINOv2\u2013DPT relative-depth network is fixed, while a multiscale adapter, DINO detection branch, and pixel-wise spatial affine calibration head are trained. Ground-truth bounding boxes restrict metric supervision to target-relevant regions but are not inputs to the calibration head. At evaluation, all depth methods use identical predicted RoIs, and a class-aware kernel-density estimation rule extracts representative depths. On 272 hardware-synchronized RGB\u2013LiDAR frames from one operational 150-ton crane, the proposed method achieved a target-level RMSE of 0.564 m and a pixel-level inlier ratio of 0.966 for \u03b4&lt;1.25. One-to-one ground-truth matching yielded a 0.566 m target RMSE with detection precision\/recall of 0.960\/0.970. AdaBins obtained lower pixel-level MAE and RMSE, whereas the proposed method obtained lower target-level errors. These results demonstrate the feasibility of target-focused metric ranging under the evaluated operational crane conditions.<\/jats:p>","DOI":"10.3390\/s26185785","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T14:39:24Z","timestamp":1789137564000},"page":"5785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Target-Aware Decoupled Metric-Depth Estimation for Top-View Crane Safety Surveillance"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7065-5831","authenticated-orcid":false,"given":"Min Woo","family":"Woo","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, IT College, Kyungpook National University, Daegu 41566, Republic of Korea"},{"name":"Advanced Mobility and Robot System Group, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea"}],"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9799-1773","authenticated-orcid":false,"given":"Jaeil","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, IT College, Kyungpook National University, Daegu 41566, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-7154","authenticated-orcid":false,"given":"Byeong Hak","family":"Kim","sequence":"additional","affiliation":[{"name":"Advanced Mobility and Robot System Group, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","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"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aei.2018.05.003","article-title":"Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach","volume":"37","author":"Fang","year":"2018","journal-title":"Adv. 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