{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:59:42Z","timestamp":1790686782289,"version":"4.1.0"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanxi Province Basic Research Program","award":["202203021212444"],"award-info":[{"award-number":["202203021212444"]}]},{"DOI":"10.13039\/501100013805","name":"Shanxi Agricultural University Science and Technology Innovation Enhancement Project","doi-asserted-by":"publisher","award":["CXGC2023045"],"award-info":[{"award-number":["CXGC2023045"]}],"id":[{"id":"10.13039\/501100013805","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013794","name":"Shanxi Postgraduate Education and Teaching Reform Project Fund","doi-asserted-by":"publisher","award":["2022YJJG094"],"award-info":[{"award-number":["2022YJJG094"]}],"id":[{"id":"10.13039\/501100013794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013805","name":"Shanxi Agricultural University Doctoral Research Start-Up Project","doi-asserted-by":"publisher","award":["2021BQ88"],"award-info":[{"award-number":["2021BQ88"]}],"id":[{"id":"10.13039\/501100013805","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013805","name":"Shanxi Agricultural University Academic Restoration Research Project","doi-asserted-by":"publisher","award":["2020xshf38"],"award-info":[{"award-number":["2020xshf38"]}],"id":[{"id":"10.13039\/501100013805","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013805","name":"Young and Middle-Aged Top-Notch Innovative Talent Cultivation Program of the Software College, Shanxi Agricultural University","doi-asserted-by":"publisher","award":["SXAUKY2024005"],"award-info":[{"award-number":["SXAUKY2024005"]}],"id":[{"id":"10.13039\/501100013805","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013805","name":"Teaching Reform Project of Shanxi Agricultural University","doi-asserted-by":"publisher","award":["JG-202523"],"award-info":[{"award-number":["JG-202523"]}],"id":[{"id":"10.13039\/501100013805","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Reliable cattle body-part localization provides visual inputs for downstream posture, locomotion, and behavior analysis in precision livestock farming. However, complex barn environments introduce scale variation, occlusion, illumination changes, and rail-like background interference. DGF\u2013YOLOv12n\u2013LiteP2 is presented as a YOLOv12n-based detector that combines dynamic fusion of local and contextual features with a lightweight stride-4 prediction branch. The dynamic gated fusion module integrates multi-kernel local feature extraction, progressive receptive-field aggregation, and input-conditioned channel selection. LiteP2 preserves high-resolution spatial information by reusing the backbone P2 feature without constructing a complete high-resolution feature pyramid. Experiments were conducted on a single-site dataset containing 6991 valid images and 132,302 body-part annotations. On the held-out in-domain test set, DGF\u2013YOLOv12n\u2013LiteP2 achieved 93.92% mAP50, 79.27% mAP75, and 69.88% mAP50:95. Compared with YOLOv12n, mAP50:95 increased by 9.61 percentage points. Across three training runs with different random seeds, the mean mAP50:95 reached 70.03\u00b10.30%. The model contained 11.68 M parameters and required 22.89 GFLOPs, indicating a trade-off between detection accuracy and computational cost. These results support the complementary roles of dynamic feature fusion and high-resolution prediction under the evaluated barn conditions. Further validation on additional farms, longer acquisition periods, and agricultural edge devices remains necessary.<\/jats:p>","DOI":"10.3390\/s26196156","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:57:57Z","timestamp":1790611077000},"page":"6156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DGF\u2013YOLOv12n\u2013LiteP2: Dynamic Gated Local\u2013Context Fusion with a High-Resolution Detection Head for Cattle Body-Part Detection in Complex Barns"],"prefix":"10.3390","volume":"26","author":[{"given":"Yanhong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Le","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Qingqing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Software, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Jiazi","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Jingrong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Software, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2868-6246","authenticated-orcid":false,"given":"Meng","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Hua","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"ref_1","first-page":"440","article-title":"Is precision livestock farming an engineer\u2019s daydream or nightmare, an animal\u2019s friend or foe?","volume":"2","author":"Wathes","year":"2008","journal-title":"Animal"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.2527\/af.2017.0102","article-title":"General introduction to precision livestock farming","volume":"7","author":"Berckmans","year":"2017","journal-title":"Anim. Front."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.livsci.2017.05.014","article-title":"Implementation of machine vision for detecting behaviour of cattle and pigs","volume":"202","author":"Nasirahmadi","year":"2017","journal-title":"Livest. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106143","DOI":"10.1016\/j.compag.2021.106143","article-title":"Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation","volume":"185","author":"Qiao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104885","DOI":"10.1016\/j.compag.2019.104885","article-title":"Deep cascaded convolutional models for cattle pose estimation","volume":"164","author":"Li","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gong, C., Zhang, Y., Wei, Y., Du, X., Su, L., and Weng, Z. (2022). Multicow pose estimation based on keypoint extraction. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0269259"},{"key":"ref_7","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104982","DOI":"10.1016\/j.compag.2019.104982","article-title":"FLYOLOv3 deep learning for key parts of dairy cow body detection","volume":"166","author":"Jiang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107579","DOI":"10.1016\/j.compag.2022.107579","article-title":"Cattle body detection based on YOLOv5-ASFF for precision livestock farming","volume":"204","author":"Qiao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hao, W., Ren, C., Han, M., Zhang, L., Li, F., and Liu, Z. (2023). Cattle body detection based on YOLOv5-EMA for precision livestock farming. Animals, 13.","DOI":"10.3390\/ani13223535"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_13","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_14","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_15","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO series in 2021. arXiv."},{"key":"ref_16","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-centric real-time object detectors. arXiv.","DOI":"10.52202\/085713-2627"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_22","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_24","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021, January 3\u20137). Deformable DETR: Deformable transformers for end-to-end object detection. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_25","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.Y. (2023, January 1\u20135). DINO: DETR with improved denoising anchor boxes for end-to-end object detection. Proceedings of the International Conference on Learning Representations, Kigali, Rwanda."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). DETRs beat YOLOs on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_27","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany, 8\u201314 September 2018, Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective kernel networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Huang, X., Chen, H., Zhang, W., Dai, X., Li, Y., Qing, L., and He, X. (2026). AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes. IEEE Trans. Multimed.","DOI":"10.1109\/TMM.2026.3712865"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9392","DOI":"10.1109\/TCSVT.2026.3672818","article-title":"DeCenter: Density-Center Guided Perception Enhancement for UAV Object Detection","volume":"36","author":"Shi","year":"2026","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6156\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:18:40Z","timestamp":1790684320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,28]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196156"],"URL":"https:\/\/doi.org\/10.3390\/s26196156","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,28]]}}}