{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:37:31Z","timestamp":1760233051984,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The basic theory of photogrammetry is mature and widely used in engineering. The environment in engineering is very complex, resulting in the corners or multi-line intersections being blocked and unable to be measured directly. In order to solve this problem, a prediction and optimization algorithm for intersection point of spatial multi-lines based on photogrammetry is proposed. The coordinates of points on space lines are calculated by photogrammetry algorithm. Due to the influence of image point distortion and point selection error, many lines do not strictly intersect at one point. The equations of many space lines are used to fit their initial value of intersection point. The initial intersection point is projected onto each image, and the distances between the projection point and each line on the image plane are used to weight the calculated spatial lines in combination with the information entropy. Then the intersection point coordinates are re-fitted, and the intersection point is repeatedly projected and recalculate until the error is less than the threshold value or reached the set number of iterations. Three different scenarios are selected for experiments. The experimental results show that the proposed algorithm significantly improves the prediction accuracy of the intersection point.<\/jats:p>","DOI":"10.3390\/s22249821","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T04:54:49Z","timestamp":1670993689000},"page":"9821","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction and Optimization Algorithm for Intersection Point of Spatial Multi-Lines Based on Photogrammetry"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2501-296X","authenticated-orcid":false,"given":"Chengli","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xiao","sequence":"additional","affiliation":[{"name":"CCCC Second Harbor Engineering Company Ltd., Wuhan 430040, China"},{"name":"Key Laboratory of Large-Span Bridge Construction Technology, Wuhan 430040, China"},{"name":"Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure, Wuhan 430040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3901\/JME.2011.20.069","article-title":"Evaluation Method of Remaining Fatigue Life for Crane Based on the Acquisition of the Equivalent Load Spectrum by the Artificial Neural Network","volume":"47","author":"Fan","year":"2011","journal-title":"J. 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