{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T23:04:44Z","timestamp":1774479884593,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>To address the challenges of low accuracy in volleyball individual action recognition caused by complex scenarios in volleyball sports, Faster\u2010ActionNet was proposed based on the backbone of YOLOv11. In this network, partial convolutions are adopted in both the backbone and neck modules to amplify critical feature representations while minimizing redundant computational and memory overhead. In the backbone network, the Feature Refinement and Fusion Network (FRFN) attention mechanism is integrated, which employs optimized and streamlined operations to reduce feature redundancy across channels. This enhancement significantly boosts the reconstruction quality of latent sharp images and alleviates the risk of critical feature degradation. Experiments evaluating the individual action recognition model on volleyball\u2010specific tasks have revealed superior performance, with the model of mAP attaining 88.2% accuracy and 75.6\u2009frames per second (FPS) in individual action recognition. These results have surpassed state\u2010of\u2010the\u2010art benchmarks. This model demonstrates outstanding performance in real\u2010world applications, providing valuable technical insights for improving sports action recognition and advancing computer vision technologies.<\/jats:p>","DOI":"10.1002\/itl2.70091","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T15:05:15Z","timestamp":1753196715000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Faster\u2010ActionNet: Deep Partial Convolutional Neural Networks for Volleyball Action Detection on Edge Devices"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0176-4663","authenticated-orcid":false,"given":"Shaohua","family":"Wang","sequence":"first","affiliation":[{"name":"Jilin Technology College of Electronic Information  Jilin China"}]}],"member":"311","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3232034"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.220"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.05.017"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108713"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109739"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.042494"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.217"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113293"},{"key":"e_1_2_6_12_1","first-page":"1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kingma D. 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