{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:45:50Z","timestamp":1770032750855,"version":"3.49.0"},"reference-count":9,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation.<\/jats:p>","DOI":"10.3233\/jifs-210597","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T14:23:22Z","timestamp":1633703002000},"page":"6715-6725","source":"Crossref","is-referenced-by-count":15,"title":["SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation"],"prefix":"10.1177","volume":"41","author":[{"given":"Yikun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}]},{"given":"Gongping","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"},{"name":"School of Computer, Heze University, Heze, China"}]},{"given":"Yuwen","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer, Heze University, Heze, China"}]},{"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210597_ref4","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1109\/LRA.2018.2849498","article-title":"Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network","volume":"4","author":"Dias","year":"2018","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10.3233\/JIFS-210597_ref14","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.3390\/s16081222","article-title":"Deep Fruits: A Fruit Detection System Using Deep Neural Networks","volume":"8","author":"Sa","year":"2016","journal-title":"Sensors-Basel"},{"key":"10.3233\/JIFS-210597_ref15","doi-asserted-by":"crossref","first-page":"4599","DOI":"10.3390\/s19204599","article-title":"Fruit Detection and Segmentation for Apple-Harvesting Using Visual Sensor in Orchards","volume":"20","author":"Kang","year":"2019","journal-title":"Sensors (Basel)"},{"key":"10.3233\/JIFS-210597_ref18","doi-asserted-by":"crossref","unstructured":"He K. , Gkioxari G. , R P D A et al., Mask R-CNN, 2017 IEEE International Conference on Computer Vision (ICCV) (2017), 2980\u20132988.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.3233\/JIFS-210597_ref21","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","article-title":"Transfer Learning for Visual Categorization: A Survey","volume":"5","author":"Shao","year":"2015","journal-title":"Ieee T Neur Net Lear"},{"key":"10.3233\/JIFS-210597_ref22","doi-asserted-by":"crossref","unstructured":"Lin T.Y. , Maire M. , Belongie S.J. , et al., Microsoft COCO: Common Objects in Context. 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