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To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, mAP@0.5 is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5\u200as is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8\u200as also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding.<\/jats:p>","DOI":"10.3233\/jifs-237231","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T12:01:33Z","timestamp":1711108893000},"page":"231-248","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["SCS-YOLOv5s: A cattle detection and counting method for complex breeding environment"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8102-6875","authenticated-orcid":false,"given":"Zhi","family":"Weng","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China"},{"name":"State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, China"},{"name":"College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongfei","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China"},{"name":"State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.animal.2022.100650"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.animal.2021.100429"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9224914"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2022.09.002"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.applanim.2021.105491"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106313"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105300"},{"issue":"19","key":"e_1_3_2_9_1","first-page":"177","article-title":"Design of intelligent pig counting system based on improved instance segmentation algorithm[J]","volume":"36","author":"Hu Yunge,","year":"2020","unstructured":"Hu Yunge,Cang YanQiao Yulong,, Design of intelligent pig counting system based on improved instance segmentation algorithm[J], Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE)36(19) (2020), 177\u2013183.","journal-title":"Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE)"},{"issue":"10","key":"e_1_3_2_10_1","first-page":"252","article-title":"High-density Pig Herd Counting Method Combined with Feature Pyramid and Deformable Convolution[J]","volume":"53","author":"Wang Rong,","year":"2022","unstructured":"Wang Rong,Gao Ronghua,Li Qifeng,, et al. 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