{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:14:41Z","timestamp":1768014881989,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T00:00:00Z","timestamp":1634947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002462","name":"Chungnam National University","doi-asserted-by":"publisher","award":["2021-0861-01"],"award-info":[{"award-number":["2021-0861-01"]}],"id":[{"id":"10.13039\/501100002462","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is necessary to locate microplastic particles mixed with beach sand to be able to separate them. This paper illustrates a kernel weight histogram-based analytical process to determine an appropriate neural network to perform tiny object segmentation on photos of sand with a few microplastic particles. U-net and MultiResUNet are explored as target networks. However, based on our observation of kernel weight histograms, visualized using TensorBoard, the initial encoder stages of U-net and MultiResUNet are useful for capturing small features, whereas the later encoder stages are not useful for capturing small features. Therefore, we derived reduced versions of U-net and MultiResUNet, such as Half U-net, Half MultiResUNet, and Quarter MultiResUNet. From the experiment, we observed that Half MultiResUNet displayed the best average recall-weighted F1 score (40%) and recall-weighted mIoU (26%) and Quarter MultiResUNet the second best average recall-weighted F1 score and recall-weighted mIoU for our microplastic dataset. They also require 1\/5 or less floating point operations and 1\/50 or a smaller number of parameters over U-net and MultiResUNet.<\/jats:p>","DOI":"10.3390\/s21217030","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"7030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Neural Network Analysis for Microplastic Segmentation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3023-4230","authenticated-orcid":false,"given":"Gwanghee","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Chungnam National University, Daejeon 34134, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5659-0503","authenticated-orcid":false,"given":"Kyoungson","family":"Jhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Chungnam National University, Daejeon 34134, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,23]]},"reference":[{"key":"ref_1","unstructured":"Rogers, K. 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