{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:16:54Z","timestamp":1774628214493,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi provincial key innovation-driven projects","award":["AA18118002-3"],"award-info":[{"award-number":["AA18118002-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Part cleaning is very important for the assembly of precision machinery. After cleaning, the parts are randomly distributed in the collection area, which makes it difficult for a robot to collect them. Common robots can only collect parts located in relatively fixed positions, and it is difficult to adapt these robots to collect at randomly distributed positions. Therefore, a rapid part classification method based on a non-pooling YOLOv5 network for the recognition of randomly distributed multiple types of parts is proposed in this paper; this method classifies parts from their two-dimensional images obtained using industrial cameras. We compared the traditional and non-pooling YOLOv5 networks under different activation functions. Experimental results showed that the non-pooling YOLOv5 network improved part recognition precision by 8% and part recall rate by 3% within 100 epochs of training, which helped improve the part classification efficiency. The experiment showed that the non-pooling YOLOv5 network exhibited improved classification of industrial parts compared to the traditional YOLOv5 network.<\/jats:p>","DOI":"10.3390\/s22239335","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"9335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Research on Non-Pooling YOLOv5 Based Algorithm for the Recognition of Randomly Distributed Multiple Types of Parts"],"prefix":"10.3390","volume":"22","author":[{"given":"Zehua","family":"Yu","sequence":"first","affiliation":[{"name":"Guangxi\u2019s Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi\u2019s Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Xingyu","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangxi\u2019s Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Yang","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangxi\u2019s Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Xiaoke","family":"Liu","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tekin, B., Sinha, S.N., and Fua, P. 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