{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:15:52Z","timestamp":1785417352918,"version":"3.56.0"},"reference-count":55,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2022R1C1C1011743"],"award-info":[{"award-number":["NRF-2022R1C1C1011743"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"award":["NRF-2022R1C1C1011743"],"award-info":[{"award-number":["NRF-2022R1C1C1011743"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Object detection is a core component of industrial vision systems in manufacturing, infrastructure monitoring, and safety-critical sensing. While the mean average precision (mAP) averages the performance over all confidence thresholds, real-world deployment demands committing to a single operating threshold under score imprecision, distribution shifts, and asymmetric\u2014often only approximately known\u2014error costs. From a soft-computing perspective, deployment should explicitly manage this uncertainty rather than rely on a static validation optimum. We propose domain-specific and robust localization recall precision (DSR-LRP), a three-phase decision-support framework. The framework elicits soft domain preferences\u2014such as asymmetric error costs, tolerable localization imprecision, and expected perturbations\u2014from practitioner knowledge and encodes them as three quantitative parameters (k, \u03b1IoU, \u03b2). A cost-sensitive, threshold-local objective aggregates the performance within a robustness band around each candidate threshold, jointly capturing the accuracy and local stability. Finally, it yields an interpretable recommendation package comprising the operating threshold, its DSR-LRP score, and visual evidence. Experiments on four practical datasets (blood cell screening, wildfire smoke monitoring, pothole detection, and semiconductor sensor inspection) showed that DSR-LRP consistently selected operating thresholds that were robust and cost-aligned. For example, in pothole detection, an LRP-optimal threshold degraded by 15.6% under simulated shifts, while the DSR-LRP recommendation changed by only 1.8%. DSR-LRP complements global metrics such as the mAP and provides a soft-computing-oriented tool for reliable, evidence-driven deployment of industrial object detectors.<\/jats:p>","DOI":"10.3390\/a19050409","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T11:53:10Z","timestamp":1779191590000},"page":"409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Managing Cost\u2013Stability Trade-Offs in Industrial Object Detection: A Unified Decision Support Framework"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4029-0238","authenticated-orcid":false,"given":"Kuhyun","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Semiconductor and Display Engineering, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyunggi-do, Republic of Korea"},{"name":"Samsung Institute of Technology, 1, Samsung-ro, Yongin-si 17113, Gyunggi-do, Republic of Korea"},{"name":"Memory Division, Samsung Electronics Co., Ltd., 1-1, Samsungjeonja-ro, Hwaseong-si 18448, Gyunggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihoon","family":"Hong","sequence":"additional","affiliation":[{"name":"Memory Division, Samsung Electronics Co., Ltd., 1-1, Samsungjeonja-ro, Hwaseong-si 18448, Gyunggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9722-4880","authenticated-orcid":false,"given":"Beom-Seok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyunggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6760-3332","authenticated-orcid":false,"given":"Yuna","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyunggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8549-8992","authenticated-orcid":false,"given":"Dong-Hee","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyunggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. 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