{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T20:57:52Z","timestamp":1760821072619,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T00:00:00Z","timestamp":1566950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["No. 91420202"],"award-info":[{"award-number":["No. 91420202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The gradual application of deep learning in the field of computer vision and image processing has made great breakthroughs. Applications such as object detection, recognition and image semantic segmentation have been improved. In this study, to measure the distance of the vehicle ahead, a preceding vehicle ranging system based on fitting method was designed. First obtaining an accurate bounding box frame in the vehicle detection, the Mask R-CNN (region-convolutional neural networks) algorithm was improved and tested in the BDD100K (Berkeley deep derive) asymmetry dataset. This method can shorten vehicle detection time by 33% without reducing the accuracy. Then, according to the pixel value of the bounding box in the image, the fitting method was applied to the vehicle monocular camera for ranging. Experimental results demonstrate that the method can measure the distance of the preceding vehicle effectively, with a ranging error of less than 10%. The accuracy of the measurement results meets the requirements of collision warning for safe driving.<\/jats:p>","DOI":"10.3390\/sym11091081","type":"journal-article","created":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T11:23:18Z","timestamp":1566991398000},"page":"1081","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Visual Meterstick: Preceding Vehicle Ranging Using Monocular Vision Based on the Fitting Method"],"prefix":"10.3390","volume":"11","author":[{"given":"Chaochao","family":"Meng","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"}]},{"given":"Hong","family":"Bao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-8322","authenticated-orcid":false,"given":"Yan","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"},{"name":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-6273","authenticated-orcid":false,"given":"Xinkai","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"}]},{"given":"Yuqing","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,28]]},"reference":[{"key":"ref_1","first-page":"314","article-title":"Research on monocular vision measurement technology","volume":"25","author":"Huang","year":"2004","journal-title":"Acta Metrol. 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