{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:34:59Z","timestamp":1769751299804,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683560","type":"print"},{"value":"9781643683577","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,24]]},"abstract":"<jats:p>With the help of the space-to-depth and depth-to-space modules, we provide a convolutional neural network design for depth estimation. We show designs that down sample the spatial information of the picture utilizing space-to-depth (SD) as opposed to the widely used pooling methods (Max-pooling and Average-pooling). The space-to-depth module may shrink the image while maintaining the spatial information of the image in the form of additional depth information. This technique is far superior to Max-pooling, which diminishes the image\u2019s information and features. We also suggest a lightweight decoder step that builds a high-resolution depth map out of many low-resolution feature maps using the depth-to-space (DS) module. The suggested architecture effectively learns depth estimation with high processing speed and accuracy. We trained and evaluated our suggested model on NYU-depthV2 dataset and attained low error values (RMSE=0.342) and high delta accuracies (\u03b43=0.996) at a fast-processing speed (25Fps).<\/jats:p>","DOI":"10.3233\/faia220423","type":"book-chapter","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T09:25:47Z","timestamp":1669368347000},"source":"Crossref","is-referenced-by-count":2,"title":["SD-Depth: Light-Weight Monocular Depth Estimation Using Space Depth CNN for Real-Time Applications"],"prefix":"10.3233","author":[{"given":"Hatem","family":"Ibrahem","sequence":"first","affiliation":[{"name":"School of Information and Communication, Chungbuk National University, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Salem","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Chungbuk National University, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun-Soo","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Chungbuk National University, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220423","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T09:25:48Z","timestamp":1669368348000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,24]]},"ISBN":["9781643683560","9781643683577"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220423","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,24]]}}}