{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T06:00:03Z","timestamp":1769061603834,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1F1A1062775"],"award-info":[{"award-number":["2022R1F1A1062775"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021-DD-SB-0533-01"],"award-info":[{"award-number":["2021-DD-SB-0533-01"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Innovation Foundation","award":["2022R1F1A1062775"],"award-info":[{"award-number":["2022R1F1A1062775"]}]},{"name":"Korea Innovation Foundation","award":["2021-DD-SB-0533-01"],"award-info":[{"award-number":["2021-DD-SB-0533-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered.<\/jats:p>","DOI":"10.3390\/s22218315","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"8315","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information"],"prefix":"10.3390","volume":"22","author":[{"given":"Seungwook","family":"Son","sequence":"first","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3969-2879","authenticated-orcid":false,"given":"Hanse","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hwapyeong","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seunghyun","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yooil","family":"Suh","sequence":"additional","affiliation":[{"name":"Info Valley Korea Co., Ltd., Anyang 14067, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-2959","authenticated-orcid":false,"given":"Sungju","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software, Sangmyung University, Cheonan 31066, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"ref_1","unstructured":"OECD (2022, September 30). Meat Consumption (Indicator). Available online: https:\/\/www.oecd-ilibrary.org\/agriculture-and-food\/meat-consumption\/indicator\/english_fa290fd0-en."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107227","DOI":"10.1016\/j.compag.2022.107227","article-title":"Barriers to computer vision applications in pig production facilities","volume":"200","author":"Jiangong","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.compag.2013.01.013","article-title":"Automatic identification of marked pigs in a pen using image pattern recognition","volume":"93","author":"Kashiha","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.biosystemseng.2013.06.011","article-title":"Foreground detection using loopy belief propagation","volume":"116","author":"Tu","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_5","first-page":"555","article-title":"Automatic monitoring of pig activity using image analysis","volume":"159","author":"Kashiha","year":"2013","journal-title":"Livest. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.livsci.2013.12.011","article-title":"Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities","volume":"160","author":"Ott","year":"2014","journal-title":"Livest. Sci."},{"key":"ref_7","first-page":"1481","article-title":"A cost-effective pigsty monitoring system based on a video sensor","volume":"8","author":"Chung","year":"2014","journal-title":"KSII Trans. Internet Inf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.livsci.2013.11.007","article-title":"Automatic monitoring of pig locomotion using image analysis","volume":"159","author":"Kashiha","year":"2014","journal-title":"Livest. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12407","DOI":"10.3390\/s150921407","article-title":"Illumination and reflectance estimation with its application in foreground","volume":"15","author":"Tu","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.biosystemseng.2015.05.001","article-title":"Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation","volume":"135","author":"Guo","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.compag.2016.04.022","article-title":"Automation detection of mounting behaviours among pigs using image analysis","volume":"124","author":"Nasirahmadi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lee, J., Jin, L., Park, D., and Chung, Y. (2016). Automatic recognition of aggressive behavior in pigs using a kinect depth sensor. Sensors, 16.","DOI":"10.3390\/s16050631"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2015.10.002","article-title":"Monitoring pig movement at the slaughterhouse using optical flow and modified angular histogram","volume":"141","author":"Gronskyte","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_14","first-page":"145","article-title":"Boundary detection of pigs in pens based on adaptive thresholding using an integral image and adaptive partitioning","volume":"16","author":"Buayai","year":"2017","journal-title":"CMU J. Nat. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, J., Chung, Y., Choi, Y., Sa, J., Kim, H., Chung, Y., Park, D., and Kim, H. (2017). Depth-based detection of standing-pigs in moving noise environments. Sensors, 17.","DOI":"10.3390\/s17122757"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gray, H., Ye, X., Collins, L., and Allinson, N. (2018). Automatic individual pig detection and tracking in surveillance videos. arXiv.","DOI":"10.3390\/s19051188"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compag.2018.06.043","article-title":"Model-based detection of pigs in images under sub-optimal conditions","volume":"152","author":"Traulsen","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104840","DOI":"10.1016\/j.compag.2019.05.049","article-title":"Automated pig counting using deep learning","volume":"163","author":"Tian","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.biosystemseng.2019.02.018","article-title":"Group-housed pig detection in video surveillance of overhead views using multi-feature template matching","volume":"181","author":"Li","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nasirahmadi, A., Sturm, B., Edwards, S., Jeppsson, K., Olsson, A., M\u00fcller, S., and Hensel, O. (2019). Deep learning and machine vision approaches for posture detection of individual pigs. Sensors, 19.","DOI":"10.3390\/s19173738"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Psota, E., Mittek, M., P\u00e9rez, L., Schmidt, T., and Mote, B. (2019). Multi-Pig Part Detection and Association with a Fully-Convolutional Network. Sensors, 19.","DOI":"10.3390\/s19040852"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hong, M., Ahn, H., Atif, O., Lee, J., Park, D., and Chung, Y. (2020). Field-applicable pig anomaly detection system using vocalization for embedded board implementations. Appl. Sci., 10.","DOI":"10.3390\/app10196991"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105580","DOI":"10.1016\/j.compag.2020.105580","article-title":"A computer vision approach for recognition of the engagement of pigs with different enrichment objects","volume":"175","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13665","DOI":"10.1038\/s41598-020-70688-6","article-title":"Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs","volume":"10","author":"Alameer","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105391","DOI":"10.1016\/j.compag.2020.105391","article-title":"Automatically detecting pig position and posture by 2D camera imaging and deep learning","volume":"174","author":"Riekert","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Br\u00fcnger, J., Gentz, M., Traulsen, I., and Koch, R. (2020). Panoptic segmentation of individual pigs for posture recognition. Sensors, 20.","DOI":"10.3390\/s20133710"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ahn, H., Son, S., Kim, H., Lee, S., Chung, Y., and Park, D. (2021). EnsemblePigDet: Ensemble deep learning for accurate pig detection. Appl. Sci., 11.","DOI":"10.3390\/app11125577"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106417","DOI":"10.1016\/j.compag.2021.106417","article-title":"Center clustering network improves piglet counting under occlusion","volume":"189","author":"Huang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106213","DOI":"10.1016\/j.compag.2021.106213","article-title":"Model selection for 24\/7 pig position and posture detection by 2D camera imaging and deep learning","volume":"187","author":"Riekert","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106140","DOI":"10.1016\/j.compag.2021.106140","article-title":"Dual attention-guided feature pyramid network for instance segmentation of group pigs","volume":"186","author":"Hu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cag.2021.01.004","article-title":"Pig-net: Inception based deep learning architecture for 3d point cloud segmentation","volume":"95","author":"Hegde","year":"2021","journal-title":"Comput. Graphics."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shao, H., Pu, J., and Mu, J. (2021). Pig-posture recognition based on computer vision: Dataset and exploration. Animals, 11.","DOI":"10.3390\/ani11051295"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ocepek, M., \u017dnidar, A., Lavri\u010d, M., and \u0160korjanc, D. (2022). DigiPig: First developments of an automated monitoring system for body, head, and tail detection in intensive pig farming. Agriculture, 12.","DOI":"10.3390\/agriculture12010002"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kim, J., Suh, Y., Lee, J., Chae, H., Ahn, H., Chung, Y., and Park, D. (2022). EmbeddedPigCount: Pig counting with video object detection and tracking on an embedded board. Sensors, 22.","DOI":"10.3390\/s22072689"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bo, Z., Atif, O., Lee, J., Park, D., and Chung, Y. (2022). GAN-Based video denoising with attention mechanism for field-applicable pig detection system. Sensors, 22.","DOI":"10.3390\/s22103917"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ji, H., Yu, J., Lao, F., Zhuang, Y., Wen, Y., and Teng, G. (2022). Automatic position detection and posture recognition of grouped pigs based on deep learning. Agriculture, 12.","DOI":"10.3390\/agriculture12091314"},{"key":"ref_37","first-page":"3212","article-title":"Object detection with deep learning: A review","volume":"99","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_40","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_41","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., and Liao, H. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_43","unstructured":"(2022, September 20). Open Source Computer Vision: \u2018OpenCV\u2019. Available online: http:\/\/opencv.org."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K. (1994). Contrast Limited Adaptive Histogram Equalization, Academic Press Inc.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"ref_45","unstructured":"(2022, September 30). Hanwha Surveillance Camera. Available online: https:\/\/www.hanwhasecurity.com\/product\/qno-6012r\/."},{"key":"ref_46","unstructured":"NVIDIA (2022, September 30). NVIDIA Jetson TX2. Available online: http:\/\/www.nvidia.com\/object\/embedded-systems-dev-kitsmodules.html."},{"key":"ref_47","unstructured":"Intel (2022, September 30). Intel RealSense D435. Available online: https:\/\/www.intelrealsense.com\/depth-camera-d435."},{"key":"ref_48","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkareit, J., Jones, L., Gomez, A., Kaiser, G., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the NeurIPS, Long Beach, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8315\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:52Z","timestamp":1760144752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,29]]},"references-count":48,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218315"],"URL":"https:\/\/doi.org\/10.3390\/s22218315","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,29]]}}}