{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:54:04Z","timestamp":1772823244759,"version":"3.50.1"},"reference-count":7,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100018529","name":"Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province","doi-asserted-by":"publisher","award":["2023SJYB1259"],"award-info":[{"award-number":["2023SJYB1259"]}],"id":[{"id":"10.13039\/501100018529","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Research Project of Cooperation between Industry and University in Jiangsu Province of China","award":["BY20230791"],"award-info":[{"award-number":["BY20230791"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p> The integration of Visual Transformers (ViTs) with Faster R-CNN has shown significant promise in computer vision tasks requiring both high accuracy and efficient object detection. However, the computational cost and resource requirements of these models often limit their application in real time, resource-constrained environments. This paper proposes a novel optimization strategy for integrating ViT with Faster R-CNN to enhance both performance and efficiency. We introduce an improved ViT-Tiny backbone with a hybrid attention mechanism, CS-attention, that combines high- and low-frequency attention to better capture local and global features while minimizing computational overhead. Additionally, a pyramid feature network (FPN) is incorporated to enhance multi-scale feature extraction, allowing the model to accurately detect objects at varying scales. Experimental results demonstrate that the optimized model achieves high accuracy and real-time processing capabilities, making it suitable for deployment in industrial and edge computing applications. The proposed approach is validated through extensive experiments, providing a general solution for efficient object detection across various domains. <\/jats:p>","DOI":"10.1142\/s0218001425590086","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:25:18Z","timestamp":1743827118000},"source":"Crossref","is-referenced-by-count":1,"title":["Optimizing Visual Transformer and Faster R-CNN Integration for Efficient Object Detection"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9133-4738","authenticated-orcid":false,"given":"Lin","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Computer Information Technology, Wuhan Institute of Shipbuilding Technology, Wuhan 430051, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4146-4403","authenticated-orcid":false,"given":"Yun","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Information Technology, Wuhan Institute of Shipbuilding Technology, Wuhan 430051, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"S0218001425590086BIB003","doi-asserted-by":"publisher","DOI":"10.36001\/phme.2016.v3i1.1577"},{"key":"S0218001425590086BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"S0218001425590086BIB005","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Renqian L.","year":"2018"},{"key":"S0218001425590086BIB006","first-page":"91","volume":"28","author":"Ren S.","year":"2015","journal-title":"Proc. Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"S0218001425590086BIB007","volume-title":"Proc. IEEE Int. Conf. Computer Vision (ICCV)","author":"Ross G.","year":"2015"},{"key":"S0218001425590086BIB008","volume-title":"European Conf. Computer Vision (ECCV)","author":"Wei L.","year":"2016"},{"key":"S0218001425590086BIB009","volume-title":"IEEE Conf. Computer Vision and Pattern Recognition (CVPR)","author":"Xu Y.","year":"2021"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001425590086","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T02:31:28Z","timestamp":1747276288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218001425590086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":7,"journal-issue":{"issue":"06","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1142\/S0218001425590086"],"URL":"https:\/\/doi.org\/10.1142\/s0218001425590086","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]},"article-number":"2559008"}}