{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T06:10:05Z","timestamp":1773123005483,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>For both pigs in commercial farms and biological experimental pigs at breeding bases, mounting behaviour is likely to cause damage such as epidermal wounds, lameness and fractures, and will no doubt reduce animal welfare. The purpose of this paper is to develop an efficient learning algorithm that is able to detect the mounting behaviour of pigs based on the data characteristics of visible light images. Four minipigs were selected as experimental subjects and were monitored for a week by a camera that overlooked the pen. The acquired videos were analysed and the frames containing mounting behaviour were intercepted as positive samples of the dataset, and the images with inter-pig adhesion and separated pigs were taken as negative samples. Pig segmentation network based on Mask Region-Convolutional Neural Networks (Mask R-CNN) was applied to extract individual pigs in the frames. The region of interest (RoI) parameters and mask coordinates of each pig, from which eigenvectors were extracted, could be obtained. Subsequently, the eigenvectors were classified with a kernel extreme learning machine (KELM) to determine whether mounting behaviour has occurred. The pig segmentation presented considerable accuracy and mean pixel accuracy (MPA) with 94.92% and 0.8383 respectively. The presented method showed high accuracy, sensitivity, specificity and Matthews correlation coefficient with 91.47%, 95.2%, 88.34% and 0.8324 respectively. This method can be an efficient way of solving the problem of segmentation difficulty caused by partial occlusion and adhesion of pig bodies, even if the pig body colour was similar to the background, in recognition of mounting behaviour.<\/jats:p>","DOI":"10.3390\/s19224924","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T09:11:27Z","timestamp":1573636287000},"page":"4924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Mounting Behaviour Recognition for Pigs Based on Deep Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3656-1358","authenticated-orcid":false,"given":"Dan","family":"Li","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Yifei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Kaifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Zhenbo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"key":"ref_1","first-page":"109","article-title":"Aggressive and sexual behaviour of growing and finishing pigs reared in groups, without castration","volume":"56","author":"Rydhmer","year":"2006","journal-title":"Acta Agric. Scand. Sect. A"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.yhbeh.2007.03.013","article-title":"Sexual behavior of male pigs","volume":"52","author":"Hemsworth","year":"2007","journal-title":"Horm. Behav."},{"key":"ref_3","unstructured":"Rydhmer, L., Zamaratskaia, G., Andersson, H.K., Algers, B., and Lundstr\u00f6m, K. (2004, January 5\u20139). Problems with aggressive and sexual behaviour when rearing entire male pigs. Proceedings of the 55th Annual Meeting of the European Association for Animal Production, Bled, Slovenia."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2015.09.021","article-title":"Pig herd monitoring and undesirable tripping and stepping prevention","volume":"119","author":"Gronskyte","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","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_6","unstructured":"Zhu, W., and Zhang, J. (2010, January 20\u201321). Identification of Abnormal Gait of Pigs Based on Video Analysis. Proceedings of the 3rd International Symposium on Knowledge Acquisition and Modeling, Wuhan, China."},{"key":"ref_7","unstructured":"Wu, Y. (2014). Detection of Pig Lame Walk Based on Star Skeleton Model. [Master\u2019s Thesis, Jiangsu University]."},{"key":"ref_8","unstructured":"Li, Z.Y. (2013). Study on Moving Object Detection and Tracking Technology in the Application of Pig Behavior Monitoring. [Master\u2019s Thesis, China Agricultural University]."},{"key":"ref_9","first-page":"246","article-title":"Automatic tracking of pig feeding behavior based on particle filter with multi-feature fusion","volume":"33","author":"Li","year":"2017","journal-title":"Trans. CSAE"},{"key":"ref_10","unstructured":"Zhu, W., and Wu, Z. (2010, January 20\u201321). Detection of Porcine Respiration Based on Machine Vision. Proceedings of the 3rd International Symposium on Knowledge Acquisition and Modeling, Wuhan, China."},{"key":"ref_11","unstructured":"Tan, H.L. (2017). Recognition Method of Identification and Drinking Behavior for Individual Pigs Based on Machine Vision. [Master\u2019s Thesis, Jiangsu University]."},{"key":"ref_12","first-page":"250","article-title":"Sick pig behavior monitor system based on symmetrical pixel block recognition","volume":"35","author":"Pu","year":"2009","journal-title":"Comput. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1017\/S1751731116001208","article-title":"A new approach for categorizing pig lying behavior based on a Delaunay triangulation method","volume":"11","author":"Nasirahmadi","year":"2017","journal-title":"Animal"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1049\/iet-cvi.2017.0085","article-title":"Tracking of group-housed pigs using multi-ellipsoid expectation maximization","volume":"12","author":"Mateusz","year":"2018","journal-title":"IET Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2018.01.023","article-title":"Automatic recognition of lactating sow postures from depth images by deep learning dector","volume":"147","author":"Zheng","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","first-page":"189","article-title":"Lactating sow postures recognition from depth image of videos based on improved Faster R-CNN","volume":"34","author":"Xue","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.biosystemseng.2018.09.011","article-title":"Automatic recognition of sow nursing behaviour using deep learning-based segmentation and spatial and temporal feature","volume":"175","author":"Aqing","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.compag.2018.11.002","article-title":"Feeding behavior recognition for group-housed pigs with the Faster R-CNN","volume":"155","author":"Qiumei","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","first-page":"232","article-title":"Pig drinking behavior recognition based on machine vision","volume":"49","author":"Qiumei","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_21","first-page":"192","article-title":"Multi target pigs tracking loss correction algorithm based on Faster R-CNN","volume":"11","author":"Sun","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.compag.2016.04.022","article-title":"Automatic detection of mounting behaviours among pigs using image analysis","volume":"124","author":"Abozar","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K.M., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201327). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H., Dai, J., Ji, X., and Wei, Y. (2017, January 21\u201326). Fully convolutional instance-aware semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref_25","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 (NIPS), Montreal, QC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201312). Fast R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","first-page":"985","article-title":"Extreme learning machine: A new learning scheme of feedforward neural networks","volume":"2","author":"Huang","year":"2004","journal-title":"Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.3390\/ijms20081964","article-title":"mACPred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides","volume":"20","author":"Vinothini","year":"2019","journal-title":"Int. J. Mol. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1016\/j.csbj.2019.06.024","article-title":"AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees","volume":"17","author":"Balachandran","year":"2019","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.omtn.2019.08.011","article-title":"SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome","volume":"18","author":"Shaherin","year":"2019","journal-title":"Mol. Ther. Nucleic Acids"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4924\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:33:50Z","timestamp":1760189630000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4924"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,12]]},"references-count":33,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224924"],"URL":"https:\/\/doi.org\/10.3390\/s19224924","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,12]]}}}