{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:01:02Z","timestamp":1780441262157,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022ZD0115805"],"award-info":[{"award-number":["2022ZD0115805"]}]},{"name":"National Key R&amp;D Program of China","award":["2022A02011"],"award-info":[{"award-number":["2022A02011"]}]},{"name":"Provincial Key S&amp;T Program of Xinjiang","award":["2022ZD0115805"],"award-info":[{"award-number":["2022ZD0115805"]}]},{"name":"Provincial Key S&amp;T Program of Xinjiang","award":["2022A02011"],"award-info":[{"award-number":["2022A02011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Farm aerial survey layers can assist in unmanned farm operations, such as planning paths and early warnings. To address the inefficiencies and high costs associated with traditional layer construction, this study proposes a high-precision instance segmentation algorithm based on SparseInst. Considering the structural characteristics of farm elements, this study introduces a multi-scale attention module (MSA) that leverages the properties of atrous convolution to expand the sensory field. It enhances spatial and channel feature weights, effectively improving segmentation accuracy for large-scale and complex targets in the farm through three parallel dense connections. A bottom-up aggregation path is added to the feature pyramid fusion network, enhancing the model\u2019s ability to perceive complex targets such as mechanized trails in farms. Coordinate attention blocks (CAs) are incorporated into the neck to capture richer contextual semantic information, enhancing farm aerial imagery scene recognition accuracy. To assess the proposed method, we compare it against existing mainstream object segmentation models, including the Mask R-CNN, Cascade\u2013Mask, SOLOv2, and Condinst algorithms. The experimental results show that the improved model proposed in this study can be adapted to segment various complex targets in farms. The accuracy of the improved SparseInst model greatly exceeds that of Mask R-CNN and Cascade\u2013Mask and is 10.8 and 12.8 percentage points better than the average accuracy of SOLOv2 and Condinst, respectively, with the smallest number of model parameters. The results show that the model can be used for real-time segmentation of targets under complex farm conditions.<\/jats:p>","DOI":"10.3390\/s24185990","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T11:36:37Z","timestamp":1726486597000},"page":"5990","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Improved Instance Segmentation Method for Complex Elements of Farm UAV Aerial Survey Images"],"prefix":"10.3390","volume":"24","author":[{"given":"Feixiang","family":"Lv","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taihong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Ministry of Education Engineering, Research Center for Intelligent Agriculture, Urumqi 830052, China"},{"name":"Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5430-4390","authenticated-orcid":false,"given":"Yunjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Ministry of Education Engineering, Research Center for Intelligent Agriculture, Urumqi 830052, China"},{"name":"Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Ministry of Education Engineering, Research Center for Intelligent Agriculture, Urumqi 830052, China"},{"name":"Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Urvina, R.P., Guevara, C.L., V\u00e1sconez, J.P., and Prado, A.J. 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