{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:58:42Z","timestamp":1780880322254,"version":"3.54.1"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.displa.2026.103514","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:59:16Z","timestamp":1778284756000},"page":"103514","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ResFuNet: A robust vision-based detection framework for robotic grasp pose estimation"],"prefix":"10.1016","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6763-1407","authenticated-orcid":false,"given":"Xujian","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xindi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103514_b1","series-title":"Lightweight convolutional neural network with Gaussian-based grasping representation for robotic grasping detection","author":"Cao","year":"2021"},{"key":"10.1016\/j.displa.2026.103514_b2","article-title":"A comprehensive study of 3-D vision-based robot manipulation","volume":"early access","author":"Cong","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.displa.2026.103514_b3","article-title":"Eye-to-Action: Real-time robotic grasping via lightweight gaze estimation","author":"Wang","year":"2026","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.displa.2026.103514_b4","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1109\/LRA.2023.3268596","article-title":"AAGDN: Attention-augmented grasp detection network based on coordinate attention and effective feature fusion method","volume":"8","author":"Zhou","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.displa.2026.103514_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105694","article-title":"State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: A review","volume":"177","author":"Zhang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.displa.2026.103514_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102189","article-title":"SISG-Net: Simultaneous instance segmentation and grasp detection for robot grasp in clutter","volume":"58","author":"Yan","year":"2023","journal-title":"Adv. Eng. Inform."},{"issue":"4","key":"10.1016\/j.displa.2026.103514_b7","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1109\/LRA.2018.2852777","article-title":"Real-world multiobject, multigrasp detection","volume":"3","author":"Chu","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.displa.2026.103514_b8","doi-asserted-by":"crossref","unstructured":"X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang, N. Zheng, Fully convolutional grasp detection network with oriented anchor box, in: Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst., IROS, 2018, pp. 7223\u20137230.","DOI":"10.1109\/IROS.2018.8594116"},{"key":"10.1016\/j.displa.2026.103514_b9","doi-asserted-by":"crossref","unstructured":"H. Zhang, X. Lan, S. Bai, X. Zhou, Z. Tian, N. Zheng, ROI-based robotic grasp detection for object overlapping scenes, in: Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst., IROS, 2019, pp. 4768\u20134775.","DOI":"10.1109\/IROS40897.2019.8967869"},{"key":"10.1016\/j.displa.2026.103514_b10","doi-asserted-by":"crossref","unstructured":"S. Wang, X. Jiang, J. Zhao, X. Wang, W. Zhou, Y. Liu, Efficient fully convolution neural network for generating pixel wise robotic grasps with high resolution images, in: Proc. IEEE Int. Conf. Robot. Biomimetics, ROBIO, 2019, pp. 474\u2013480.","DOI":"10.1109\/ROBIO49542.2019.8961711"},{"key":"10.1016\/j.displa.2026.103514_b11","first-page":"1","article-title":"Lightweight pixel-wise generative robot grasping detection based on RGB-D dense fusion","volume":"71","author":"Tian","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"10.1016\/j.displa.2026.103514_b12","first-page":"2241","article-title":"SKGNet: Robotic grasp detection with selective kernel convolution","volume":"20","author":"Sheng","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.displa.2026.103514_b13","doi-asserted-by":"crossref","unstructured":"B. Faverjon, J. Ponce, On computing two-finger force-closure grasps of curved 2D objects, in: Proc. IEEE Int. Conf. Robot. Autom, 1991, pp. 424\u2013429.","DOI":"10.1109\/ROBOT.1991.131614"},{"key":"10.1016\/j.displa.2026.103514_b14","doi-asserted-by":"crossref","unstructured":"C. Borst, M. Fischer, G. Hirzinger, Grasping the dice by dicing the grasp, in: Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst., IROS, Vol. 4, 2003, pp. 3692\u20133697.","DOI":"10.1109\/IROS.2003.1249729"},{"issue":"4\u20135","key":"10.1016\/j.displa.2026.103514_b15","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1177\/0278364914549607","article-title":"Deep learning for detecting robotic grasps","volume":"34","author":"Lenz","year":"2015","journal-title":"Int. J. Robot. Res."},{"issue":"2\u20133","key":"10.1016\/j.displa.2026.103514_b16","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0278364919859066","article-title":"Learning robust, real-time, reactive robotic grasping","volume":"39","author":"Morrison","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"10.1016\/j.displa.2026.103514_b17","doi-asserted-by":"crossref","unstructured":"J. Redmon, A. Angelova, Real-time grasp detection using convolutional neural networks, in: Proc. IEEE Int. Conf. Robot. Autom., ICRA, 2015, pp. 1316\u20131322.","DOI":"10.1109\/ICRA.2015.7139361"},{"key":"10.1016\/j.displa.2026.103514_b18","article-title":"Anchor-based multi-scale deep grasp pose detector with encoded angle regression","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.displa.2026.103514_b19","doi-asserted-by":"crossref","unstructured":"S. Kumra, S. Joshi, F. Sahin, Antipodal robotic grasping using generative residual convolutional neural network, in: Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst., IROS, 2020, pp. 9626\u20139633.","DOI":"10.1109\/IROS45743.2020.9340777"},{"issue":"2","key":"10.1016\/j.displa.2026.103514_b20","doi-asserted-by":"crossref","first-page":"5238","DOI":"10.1109\/LRA.2022.3145064","article-title":"SE-ResUNet: A novel robotic grasp detection method","volume":"7","author":"Yu","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.displa.2026.103514_b21","article-title":"MSTCNet: Multiscale transformer-CNN network for robotic grasp detection","author":"Li","year":"2025","journal-title":"IEEE\/ASME Trans. Mechatronics"},{"issue":"4","key":"10.1016\/j.displa.2026.103514_b22","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1109\/TASE.2021.3108800","article-title":"PPR-Net++: Accurate 6-D pose estimation in stacked scenarios","volume":"19","author":"Zeng","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"9","key":"10.1016\/j.displa.2026.103514_b23","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1109\/TPAMI.2005.173","article-title":"Canny edge detection enhancement by scale multiplication","volume":"27","author":"Bao","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103514_b24","unstructured":"zhou Zhu, Han Hu, Stephen Lin, Jifeng Dai, Deformable convnets v2: More deformable, better results, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 9308\u20139316."},{"issue":"1","key":"10.1016\/j.displa.2026.103514_b25","first-page":"3021","article-title":"Cooperative grasp detection using convolutional neural network","volume":"110","author":"Ye","year":"2023","journal-title":"J. Intell. Robot. Syst."},{"key":"10.1016\/j.displa.2026.103514_b26","series-title":"Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach","author":"Morrison","year":"2018"},{"key":"10.1016\/j.displa.2026.103514_b27","article-title":"A printed circuit board surface defect detection method for long-tail and multi-scale scenarios","volume":"165","author":"Xuangang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103514_b28","doi-asserted-by":"crossref","unstructured":"S. Kumra, C. Kanan, Robotic grasp detection using deep convolutional neural networks, in: Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst., IROS, 2017, pp. 769\u2013776.","DOI":"10.1109\/IROS.2017.8202237"},{"key":"10.1016\/j.displa.2026.103514_b29","doi-asserted-by":"crossref","unstructured":"Y. Jiang, S. Moseson, A. Saxena, Efficient grasping from RGBD images: Learning using a new rectangle representation, in: Proc. IEEE Int. Conf. Robot. Autom, 2011, pp. 3304\u20133311.","DOI":"10.1109\/ICRA.2011.5980145"},{"key":"10.1016\/j.displa.2026.103514_b30","doi-asserted-by":"crossref","unstructured":"A. Depierre, E. Dellandrea, L. Chen, Jacquard: A large scale dataset for robotic grasp detection, in: Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst., IROS, 2018, pp. 3511\u20133516.","DOI":"10.1109\/IROS.2018.8593950"},{"key":"10.1016\/j.displa.2026.103514_b31","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s00530-026-02224-x","article-title":"Mamba and wavelet-enhanced dual-modal domain adaptation for grasp detection","volume":"32","author":"Wang","year":"2026","journal-title":"Multimedia Syst."},{"key":"10.1016\/j.displa.2026.103514_b32","article-title":"MCS-ResNet: A generative robot grasping network based on RGB-D fusion","author":"Pei","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"11","key":"10.1016\/j.displa.2026.103514_b33","doi-asserted-by":"crossref","first-page":"11611","DOI":"10.1109\/TIE.2021.3120474","article-title":"High-performance pixel-level grasp detection based on adaptive grasping and grasp-aware network","volume":"69","author":"Wang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.displa.2026.103514_b34","first-page":"1","article-title":"A robot grasping system with single-stage anchor-free deep grasp detector","volume":"71","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.displa.2026.103514_b35","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.displa.2026.103514_b36","doi-asserted-by":"crossref","unstructured":"S. Woo, J. Park, J.Y. Lee, I.S. Kweon, CBAM: Convolutional block attention module, in: Proc. Eur. Conf. Comput. Vis., ECCV, 2018, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001770?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001770?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:10:15Z","timestamp":1780877415000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0141938226001770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":36,"alternative-id":["S0141938226001770"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103514","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ResFuNet: A robust vision-based detection framework for robotic grasp pose estimation","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103514","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103514"}}