{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T15:20:11Z","timestamp":1769095211469,"version":"3.49.0"},"reference-count":14,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China under Grants","award":["U20A20197"],"award-info":[{"award-number":["U20A20197"]}]},{"name":"the National Natural Science Foundation of China under Grants","award":["2020JH2\/10100040"],"award-info":[{"award-number":["2020JH2\/10100040"]}]},{"name":"the Provincial Key Research and Development for Liaoning under Grant","award":["U20A20197"],"award-info":[{"award-number":["U20A20197"]}]},{"name":"the Provincial Key Research and Development for Liaoning under Grant","award":["2020JH2\/10100040"],"award-info":[{"award-number":["2020JH2\/10100040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article proposes a CBAM-ASPP-SqueezeNet model based on the attention mechanism and atrous spatial pyramid pooling (CBAM-ASPP) to solve the problem of robot multi-target grasping detection. Firstly, the paper establishes and expends a multi-target grasping dataset, as well as introduces and uses transfer learning to conduct network pre-training on the single-target dataset and slightly modify the model parameters using the multi-target dataset. Secondly, the SqueezeNet model is optimized and improved using the attention mechanism and atrous spatial pyramid pooling module. The paper introduces the attention mechanism network to weight the transmitted feature map in the channel and spatial dimensions. It uses a variety of parallel operations of atrous convolution with different atrous rates to increase the size of the receptive field and preserve features from different ranges. Finally, the CBAM-ASPP-SqueezeNet algorithm is verified using the self-constructed, multi-target capture dataset. When the paper introduces transfer learning, the various indicators converge after training 20 epochs. In the physical grabbing experiment conducted by Kinova and SIASUN Arm, a network grabbing success rate of 93% was achieved.<\/jats:p>","DOI":"10.3390\/s23135826","type":"journal-article","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T02:34:07Z","timestamp":1687487647000},"page":"5826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Research on Small Sample Multi-Target Grasping Technology Based on Transfer Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Bin","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"},{"name":"SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China"},{"name":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengdong","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengshan","family":"Zou","sequence":"additional","affiliation":[{"name":"SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuejiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruohuai","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"ref_1","first-page":"419","article-title":"Research on Robot Dynamic Grasping Technology Based on Perspective Transformation","volume":"10","author":"Zhang","year":"2021","journal-title":"Softw. Eng. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-020-01127-9","article-title":"Learning an end-to-end spatial grasp generation and refinement algorithm from simulation","volume":"32","author":"Ni","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_3","unstructured":"Zhao, B., Wu, C., Zhang, X., Sun, R., and Jiang, Y. (2023). Object grasping network technology of robot arm based on Attention Mechanism. J. Jilin Univ., 1\u20139."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/LRA.2019.2895878","article-title":"On-Policy Dataset Synthesis for Learning Robot Grasping Policies Using Fully Convolutional Deep Networks","volume":"4","author":"Satish","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1108\/AA-07-2020-0096","article-title":"Simulation and deep learning on point clouds for robot grasping","volume":"41","author":"Wang","year":"2021","journal-title":"Assem. Autom."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"172988142199993","DOI":"10.1177\/1729881421999937","article-title":"Robot visual measurement and grasping strategy for roughcastings","volume":"18","author":"Wan","year":"2021","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s00170-020-05257-2","article-title":"Grasping pose estimation for SCARA robot based on deep learning of point cloud","volume":"108","author":"Wang","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kumra, S., Shirin, J., and Ferat, S. (2020, January 25\u201329). Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9340777"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1080\/01691864.2018.1529620","article-title":"Multiobjective evolution of deep learning parameters for robot manipulator object recognition and grasping","volume":"32","author":"Hossain","year":"2018","journal-title":"Adv. Robot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3096","DOI":"10.3390\/agronomy12123096","article-title":"A Method of Grasping Detection for Kiwifruit Harvesting Robot Based on Deep Learning","volume":"12","author":"Li","year":"2022","journal-title":"Agronomy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s10163-020-01098-z","article-title":"Deep learning of grasping detection for a robot used in sorting construction and demolition waste","volume":"23","author":"Ku","year":"2021","journal-title":"J. Mater. Cycles Waste Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, N., Guo, C., Liang, R., and Li, D. (2022). Collaborative Viewpoint Adjusting and Grasping via Deep Reinforcement Learning in Clutter Scenes. Machines, 10.","DOI":"10.3390\/machines10121135"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103757","DOI":"10.1016\/j.robot.2021.103757","article-title":"Real-time deep learning approach to visual servo control and grasp detection for autonomous robotic manipulation","volume":"139","author":"Ribeiro","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1002\/acs.3031","article-title":"Multi-target detection and grasping control for humanoid robot NAO","volume":"33","author":"Zhang","year":"2019","journal-title":"Int. J. Adapt. Control. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5826\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:58:47Z","timestamp":1760126327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,22]]},"references-count":14,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135826"],"URL":"https:\/\/doi.org\/10.3390\/s23135826","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,22]]}}}