{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T07:44:52Z","timestamp":1776757492113,"version":"3.51.2"},"reference-count":19,"publisher":"Institution of Engineering and Technology (IET)","issue":"14","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    To address the significant challenges of high false positive and false negative rates in existing algorithms for detecting cervical fluid\u2010based cells, an enhanced Yolov5s network is introduced. This paper details a novel approach that dynamically adjusts the weights of channels and the spatial attention in modules, substantially improving feature extraction from small objects and boosting the detection capabilities of the network. Furthermore, Mixup data augmentation technology is incorporated to counter the issue of imbalanced data categories in the custom dataset. The Complete Intersection over Union loss function is also employed to refine coordinate localization accuracy during training. Tested on the proprietary cervical cytology dataset, the modified Yolov5s achieves a mean Average Precision of 92.1%, surpassing the previous state\u2010of\u2010the\u2010art by 5.6%. This enhancement substantiates the efficacy of the proposed model. Code and models are accessible at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/youyi888\/yolov5_CPCA\">https:\/\/github.com\/youyi888\/yolov5_CPCA<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1049\/ipr2.13278","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T23:14:58Z","timestamp":1731366898000},"page":"4695-4703","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An automatic detection method for cervical liquid\u2010based cells based on improved Yolov5s network"],"prefix":"10.1049","volume":"18","author":[{"given":"Shen","family":"Xudong","sequence":"first","affiliation":[{"name":"China Jiliang University  Hangzhou Zhejiang China"},{"name":"Jiaxing Vocational and Technical College  Jiaxing Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Zhihua","sequence":"additional","affiliation":[{"name":"Xiamen University of Technology  Xiamen Fujian China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Yebo","sequence":"additional","affiliation":[{"name":"Jiaxing Vocational and Technical College  Jiaxing Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Xianglian","sequence":"additional","affiliation":[{"name":"Jiaxing Vocational and Technical College  Jiaxing Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Linfei","sequence":"additional","affiliation":[{"name":"Jiaxing Jingzhu Biotechnology Co., Ltd  Jiaxing Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(19)30482-6"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. et\u00a0al.:Deep residual learning for image recognition. In: Proceedings of the2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp.770\u2013778. IEEE Piscataway NJ (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_5_1","unstructured":"Redmon J. Farhadi A.:YOLOv3: An incremental improvement. arXiv:1804.02767 (2018)"},{"key":"e_1_2_8_6_1","first-page":"276","article-title":"Research on the use of YOLOv5 object detection algorithm in mask wearing recognition","volume":"6","author":"Liu Y.","year":"2020","journal-title":"World Sci. Res. 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Liao H.Y.M.:YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934 (2020)"},{"key":"e_1_2_8_17_1","doi-asserted-by":"crossref","unstructured":"Rezatofighi H. et\u00a0al.:Generalized intersection over union: A metric and a loss for bounding box regression. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.958\u2013967. 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