{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:53:07Z","timestamp":1780599187499,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Excellent Young and Middle-aged Scientific and Technological Innovation Teams at the Colleges and Universities of Hubei province","award":["T2021009"],"award-info":[{"award-number":["T2021009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During the rice quality testing process, the precise segmentation and extraction of grain pixels is a key technique for accurately determining the quality of each seed. Due to the similar physical characteristics, small particles and dense distributions of rice seeds, properly analysing rice is a difficult problem in the field of target segmentation. In this paper, a network called SY-net, which consists of a feature extractor module, a feature pyramid fusion module, a prediction head module and a prototype mask generation module, is proposed for rice seed instance segmentation. In the feature extraction module, a transformer backbone is used to improve the ability of the network to learn rice seed features; in the pyramid fusion module and the prediction head module, a six-layer feature fusion network and a parallel prediction head structure are employed to enhance the utilization of feature information; and in the prototype mask generation module, a large feature map is used to generate high-quality masks. Training and testing were performed on two public datasets and one private rice seed dataset. The results showed that SY-net achieved a mean average precision (mAP) of 90.71% for the private rice seed dataset and an average precision (AP) of 16.5% with small targets in COCO2017. The network improved the efficiency of rice seed segmentation and showed excellent application prospects in performing rice seed quality testing.<\/jats:p>","DOI":"10.3390\/s23136194","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:57:09Z","timestamp":1688695029000},"page":"6194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SY-Net: A Rice Seed Instance Segmentation Method Based on a Six-Layer Feature Fusion Network and a Parallel Prediction Head Structure"],"prefix":"10.3390","volume":"23","author":[{"given":"Sheng","family":"Ye","sequence":"first","affiliation":[{"name":"School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiju","family":"Wu","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2060-8991","authenticated-orcid":false,"given":"Huaqing","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/83.661196","article-title":"Adaptive thresholding by variational method","volume":"7","author":"Chan","year":"1998","journal-title":"IEEE Trans. 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