{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:23:00Z","timestamp":1761582180846,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p> The purpose of the study is to obtain more powerful data in the process of urban planning, and more excellent building recognition algorithms for the high-point monitoring images in the city.<jats:sup>26<\/jats:sup> A new algorithm based on Faster R-CNN (Faster Regions-Convolutional Neural Networks) is proposed, and the optimization methods involved are systematically described, and then a simple description of the Faster R-CNN algorithm is made for optimization. Subsequently, experiments are carried out on the MS COCO (Microsoft Common Objects in Context) datasets, and a series of relevant experimental demonstrations are conducted on the optimization scheme. Different features are used to extract networks; some practical optimization methods are added and the training method is modified; the speed and accuracy are paid attention to, and the expected goal of target detection is achieved. Based on this high-point monitoring image, the speed and accuracy of building recognition are greatly optimized. Urban managers will have more reliable information in urban planning, which has a positive impact on urban development. <\/jats:p>","DOI":"10.1142\/s0218213022500130","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T10:54:38Z","timestamp":1648724078000},"source":"Crossref","is-referenced-by-count":3,"title":["Influence of Building Recognition of High-point Monitoring Image by the Optimized Faster R-CNN on Urban Planning"],"prefix":"10.1142","volume":"31","author":[{"given":"Haiyuan","family":"Tang","sequence":"first","affiliation":[{"name":"College of Design Art, Hunan Institute of Technology, Hunan, China"}]},{"given":"Lige","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Design Art, Hunan Institute of Technology, Hunan, China"}]}],"member":"219","published-online":{"date-parts":[[2022,3,31]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022500130","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T10:54:54Z","timestamp":1648724094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213022500130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":0,"journal-issue":{"issue":"02","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["10.1142\/S0218213022500130"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022500130","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2022,3]]},"article-number":"2250013"}}