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Additionally, sea pearls are considered to be quite valuable, so the traditional manual counting methods are not satisfactory, as touching may cause damage to the pearls. In this paper, we conduct a comprehensive study on nine object-detection models, and the key metrics of these models are evaluated. The results indicate that using Faster R-CNN with ResNet152, which was pretrained on the pearl dataset, mAP@0.5IoU = 100% and mAP@0.75IoU = 98.83% are achieved for pearl recognition, requiring only 15.8 ms inference time with a counter after the first loading of the model. Finally, the superiority of the proposed algorithm of Faster R-CNN ResNet152 with a counter is verified through a comparison with eight other sophisticated object detectors with a counter. The experimental results on the self-made pearl image dataset show that the total loss decreased to 0.00044. Meanwhile, the classification loss and the localization loss of the model gradually decreased to less than 0.00019 and 0.00031, respectively. The robust performance of the proposed method across the pearl dataset indicates that Faster R-CNN ResNet152 with a counter is promising for natural light or artificial light peal detection and accurate counting.<\/jats:p>","DOI":"10.3390\/s22187026","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T04:49:22Z","timestamp":1663562962000},"page":"7026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PDC: Pearl Detection with a Counter Based on Deep Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7751-4120","authenticated-orcid":false,"given":"Mingxin","family":"Hou","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuehu","family":"Dong","sequence":"additional","affiliation":[{"name":"Agricultural Machinery Appraisal and Extension Station in Hainan, Haikou 570206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Guangdong Marine Equipment and Manufacturing Engineering Technology Research Center, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyan","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"South China of Marine Science and Engineering Guangdong Laboratory, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoling","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Guangdong Marine Equipment and Manufacturing Engineering Technology Research Center, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxiang","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1504\/IJBIC.2020.109674","article-title":"A novel squeeze YOLO-based real-time people counting approach","volume":"16","author":"Ren","year":"2020","journal-title":"Int. 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