{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:05:34Z","timestamp":1771697134917,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,22]],"date-time":"2018-07-22T00:00:00Z","timestamp":1532217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61673244"],"award-info":[{"award-number":["61673244"]}]},{"name":"National Nature Science Foundation of China","award":["61703240"],"award-info":[{"award-number":["61703240"]}]},{"name":"China Postdoctoral Science Foundation funded project","award":["2017M610423"],"award-info":[{"award-number":["2017M610423"]}]},{"name":"Doctor fund of Shandong Province","award":["ZR2017BF004"],"award-info":[{"award-number":["ZR2017BF004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With rapid calculation speed and relatively high accuracy, the AdaBoost-based detection framework has been successfully applied in some real applications of machine vision-based intelligent systems. The main shortcoming of the AdaBoost-based detection framework is that the off-line trained detector cannot be transfer retrained to adapt to unknown application scenes. In this paper, a new transfer learning structure based on two novel methods of supplemental boosting and cascaded ConvNet is proposed to address this shortcoming. The supplemental boosting method is proposed to supplementally retrain an AdaBoost-based detector for the purpose of transferring a detector to adapt to unknown application scenes. The cascaded ConvNet is designed and attached to the end of the AdaBoost-based detector for improving the detection rate and collecting supplemental training samples. With the added supplemental training samples provided by the cascaded ConvNet, the AdaBoost-based detector can be retrained with the supplemental boosting method. The detector combined with the retrained boosted detector and cascaded ConvNet detector can achieve high accuracy and a short detection time. As a representative object detection problem in intelligent transportation systems, the traffic sign detection problem is chosen to show our method. Through experiments with the public datasets from different countries, we show that the proposed framework can quickly detect objects in unknown application scenes.<\/jats:p>","DOI":"10.3390\/s18072386","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T02:58:56Z","timestamp":1532401136000},"page":"2386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Supplemental Boosting and Cascaded ConvNet Based Transfer Learning Structure for Fast Traffic Sign Detection in Unknown Application Scenes"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5516-2486","authenticated-orcid":false,"given":"Chunsheng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji\u2019nan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji\u2019nan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faliang","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji\u2019nan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Engineering, Dezhou University, Dezhou 253023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,22]]},"reference":[{"key":"ref_1","first-page":"142","article-title":"Traffic sign recognition using evolutionary AdaBoost detection and forest-ECOC classification","volume":"10","author":"Escalera","year":"2009","journal-title":"IEEE Trans. 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