{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T08:37:08Z","timestamp":1780562228295,"version":"3.54.1"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,22]],"date-time":"2022-05-22T00:00:00Z","timestamp":1653177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2020R1I1A3070835"],"award-info":[{"award-number":["NRF-2020R1I1A3070835"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2021R1I1A3049475"],"award-info":[{"award-number":["NRF-2021R1I1A3049475"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.<\/jats:p>","DOI":"10.3390\/s22103917","type":"journal-article","created":{"date-parts":[[2022,5,22]],"date-time":"2022-05-22T07:13:57Z","timestamp":1653203637000},"page":"3917","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhao","family":"Bo","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6165-3297","authenticated-orcid":false,"given":"Othmane","family":"Atif","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2077-4850","authenticated-orcid":false,"given":"Jonguk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,22]]},"reference":[{"key":"ref_1","unstructured":"(2022, February 08). Livestock and Poultry: World Markets and Trade|USDA Foreign Agricultural Service, Available online: https:\/\/www.fas.usda.gov\/data\/livestock-and-poultry-world-markets-and-trade."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.tvjl.2016.09.005","article-title":"Early Detection of Health and Welfare Compromises through Automated Detection of Behavioural Changes in Pigs","volume":"217","author":"Matthews","year":"2016","journal-title":"Vet. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"173796","DOI":"10.1109\/ACCESS.2019.2955761","article-title":"Practical Monitoring of Undergrown Pigs for IoT-Based Large-Scale Smart Farm","volume":"7","author":"Lee","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-17451-6","article-title":"Automated Tracking to Measure Behavioural Changes in Pigs for Health and Welfare Monitoring","volume":"7","author":"Matthews","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.anbehav.2014.02.007","article-title":"Observer Bias in Animal Behaviour Research: Can We Believe What We Score, If We Score What We Believe?","volume":"90","author":"Tuyttens","year":"2014","journal-title":"Anim. Behav."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1017\/S175173111400192X","article-title":"Growing Pigs\u2019 Drinking Behaviour: Number of Visits, Duration, Water Intake and Diurnal Variation","volume":"8","author":"Andersen","year":"2014","journal-title":"Animal"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104262","DOI":"10.1016\/j.beproc.2020.104262","article-title":"Accelerometer Systems as Tools for Health and Welfare Assessment in Cattle and Pigs\u2014A Review","volume":"181","author":"Chapa","year":"2020","journal-title":"Behav. Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1111\/tbed.12372","article-title":"Early Detection of Infection in Pigs through an Online Monitoring System","volume":"64","year":"2017","journal-title":"Transbound. Emerg. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"592","DOI":"10.5713\/ajas.14.0654","article-title":"Stress Detection and Classification of Laying Hens by Sound Analysis","volume":"28","author":"Lee","year":"2015","journal-title":"Asian-Australas. J. Anim. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Choi, Y., Atif, O., Lee, J., Park, D., and Chung, Y. (2018). Noise-Robust Sound-Event Classification System with Texture Analysis. Symmetry, 10.","DOI":"10.3390\/sym10090402"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.biosystemseng.2018.03.007","article-title":"Use of Vocalisation to Identify Sex, Age, and Distress in Pig Production","volume":"173","author":"Cordeiro","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, J., Choi, H., Park, D., Chung, Y., Kim, H.Y., and Yoon, S. (2016). Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis. Sensors, 16.","DOI":"10.3390\/s16040549"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1071\/AN13031","article-title":"Image-Processing Technique to Measure Pig Activity in Response to Climatic Variation in a Pig Barn","volume":"54","author":"Costa","year":"2013","journal-title":"Anim. Prod. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.applanim.2017.06.015","article-title":"Using Automated Image Analysis in Pig Behavioural Research: Assessment of the Influence of Enrichment Substrate Provision on Lying Behaviour","volume":"196","author":"Nasirahmadi","year":"2017","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Seo, J., Ahn, H., Kim, D., Lee, S., Chung, Y., and Park, D. (2020). EmbeddedPigDet\u2014Fast and Accurate Pig Detection for Embedded Board Implementations. Appl. Sci., 10.","DOI":"10.3390\/app10082878"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Sa, J., Choi, Y., Lee, H., Chung, Y., Park, D., and Cho, J. (2019). Fast Pig Detection with a Top-View Camera under Various Illumination Conditions. Symmetry, 11.","DOI":"10.3390\/sym11020266"},{"key":"ref_19","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_20","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":"Yang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gray, H., Ye, X., Collins, L., and Allinson, N. (2019). Automatic Individual Pig Detection and Tracking in Pig Farms. Sensors, 19.","DOI":"10.3390\/s19051188"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","article-title":"Single Image Haze Removal Using Dark Channel Prior","volume":"33","author":"He","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.1109\/TIP.2015.2446191","article-title":"A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior","volume":"24","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"DehazeNet: An End-to-End System for Single Image Haze Removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., and Yang, M.H. (2016, January 11\u201314). Single Image Dehazing via Multi-Scale Convolutional Neural Networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, Y., Huang, J., and Xie, Y. (2019, January 16\u201320). Enhanced Pix2pix Dehazing Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00835"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Engin, D., Genc, A., and Ekenel, H.K. (2018, January 18\u201322). Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00127"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, R., Pan, J., Li, Z., and Tang, J. (2018, January 18\u201322). Single Image Dehazing via Conditional Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00856"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fahim, M.A.N.I., and Jung, H.Y. (2021). Single Image Dehazing Using End-to-End Deep-Dehaze Network. Electronics, 10.","DOI":"10.3390\/electronics10070817"},{"key":"ref_31","unstructured":"Liu, X., Ma, Y., Shi, Z., and Chen, J. (November, January 27). GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_32","unstructured":"Pan, H. (2020). Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network. arXiv."},{"key":"ref_33","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H. (2020, January 7\u201312). FFA-Net: Feature Fusion Attention Network for Single Image Dehazing. Proceedings of the AAAI 2020\u201434th AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative Adversarial Networks","volume":"63","author":"Goodfellow","year":"2014","journal-title":"Commun. ACM"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13091619"},{"key":"ref_37","unstructured":"Ultralytics (2022, February 20). Yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109373","DOI":"10.1016\/j.ejrad.2020.109373","article-title":"Hierarchical Fracture Classification of Proximal Femur X-ray Images Using a Multistage Deep Learning Approach","volume":"133","author":"Tanzi","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ancuti, C., Ancuti, C.O., Timofte, R., and de Vleeschouwer, C. (2018, January 24\u201327). I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Poitiers, France.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., Timofte, R., and de Vleeschouwer, C. (2018, January 18\u201322). O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_45","unstructured":"Zagoruyko, S., and Komodakis, N. (2017, January 24\u201326). Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer. Proceedings of the 5th International Conference on Learning Representations, Toulon, France."},{"key":"ref_46","first-page":"1","article-title":"A Deep Learning Approach to Identifying Immunogold Particles in Electron Microscopy Images","volume":"11","author":"Jerez","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep Light Enhancement without Paired Supervision","volume":"30","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1007\/s11263-021-01466-8","article-title":"Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset","volume":"129","author":"Lv","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","article-title":"Semantic Foggy Scene Understanding with Synthetic Data","volume":"126","author":"Sakaridis","year":"2017","journal-title":"Int. J. Comput. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"27127","DOI":"10.1364\/OE.21.027127","article-title":"Haze Effect Removal from Image via Haze Density Estimation in Optical Model","volume":"21","author":"Yeh","year":"2013","journal-title":"Opt. Express"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ngo, D., Lee, G.D., and Kang, B. (2021). Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation. Sensors, 21.","DOI":"10.3390\/s21113896"},{"key":"ref_52","first-page":"121","article-title":"Measuring Distinct Regions of Grayscale Image Using Pixel Values","volume":"7","author":"Jeyalaksshmi","year":"2017","journal-title":"Artic. Int. J. Eng. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3917\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:16:23Z","timestamp":1760138183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3917"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,22]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103917"],"URL":"https:\/\/doi.org\/10.3390\/s22103917","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,22]]}}}