{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T11:04:32Z","timestamp":1743678272881,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030442880"},{"type":"electronic","value":"9783030442897"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-44289-7_36","type":"book-chapter","created":{"date-parts":[[2020,3,23]],"date-time":"2020-03-23T11:03:35Z","timestamp":1584961415000},"page":"381-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Use of Deep Learning for Bird Detection to Reduction of Collateral Damage in Fruit Fields"],"prefix":"10.1007","author":[{"given":"Humberto","family":"Garcia","sequence":"first","affiliation":[]},{"given":"Alberto","family":"Ochoa-Zezzatti","sequence":"additional","affiliation":[]},{"given":"Abraham","family":"Mart\u00ednez-Retamoza","sequence":"additional","affiliation":[]},{"given":"Gilberto","family":"Ochoa","sequence":"additional","affiliation":[]},{"given":"Lina","family":"Aguilar","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Oliva","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Mej\u00eda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,24]]},"reference":[{"unstructured":"Bishop, J., McKay, H., Parrott, D., Allan, J.: Review of international research literature regarding the effectiveness of auditory bird scaring techniques and potential alternatives. Central Science Laboratories (2003)","key":"36_CR1"},{"doi-asserted-by":"crossref","unstructured":"Hussain, M., Bird, J.J., Faria, D.R.: A study on CNN transfer learning for image classification. University of Aston (2018)","key":"36_CR2","DOI":"10.1007\/978-3-319-97982-3_16"},{"issue":"6","key":"36_CR3","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1109\/TSMCC.2012.2216260","volume":"42","author":"G Duan","year":"2012","unstructured":"Duan, G., Wang, H., Liu, Z., Chen, Y.: A machine learning-based framework for automatic visual inspection of microdrill bits in PCB production. IEEE Trans. Syst. Man Cybern. 42(6), 1679\u20131689 (2012)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"unstructured":"Saqib, M., Khan, S.D., Sharma, N., Blumenstein, M.: A study on detecting drones using deep convolutional neural networks. NSW Australia (2007)","key":"36_CR4"},{"doi-asserted-by":"crossref","unstructured":"Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. University of California, Berkeley, USA (2014)","key":"36_CR5","DOI":"10.1007\/978-3-319-10590-1_54"},{"doi-asserted-by":"crossref","unstructured":"Huber, A., Weiss, A.: Developing human-robot interaction for an Industry 4.0 Robot. Institute of Automation and Control Vienna University of Technology, Vienna, Austria (2017)","key":"36_CR6","DOI":"10.1145\/3029798.3038346"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks (2012)","key":"36_CR7"},{"doi-asserted-by":"crossref","unstructured":"Alom, Z., Bontupalli, V.R., Taha, T.M.: Intrusion detection using deep belief network and extreme learning machine, Department of Electrical and Computer Engineering, University of Dayton, OH, USA, Dayton (2016)","key":"36_CR8","DOI":"10.4018\/IJMSTR.2015040103"},{"doi-asserted-by":"crossref","unstructured":"Jiang, P., Chen, Y., Liu, B.: Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks (2019)","key":"36_CR9","DOI":"10.1109\/ACCESS.2019.2914929"},{"doi-asserted-by":"crossref","unstructured":"Xiao, Y., Xing, C., Zhang, T., Zhao, Z.: An intrusion detection model based on feature reduction and convolutional neural networks. In: College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R. China (2019)","key":"36_CR10","DOI":"10.1109\/ACCESS.2019.2904620"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014)","key":"36_CR11","DOI":"10.1109\/CVPR.2014.81"},{"unstructured":"Adams, S.M., Friedland, C.J.: A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management (2011)","key":"36_CR12"},{"doi-asserted-by":"crossref","unstructured":"Seo, J., Park, H.: Object recognition in very low resolution images using deep collaborative learning. School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Korea (2019)","key":"36_CR13","DOI":"10.1109\/ACCESS.2019.2941005"},{"doi-asserted-by":"crossref","unstructured":"Ma, C., An, W., Lei, Y., Guo, Y.: BV-CNNs: binary volumetric convolutional networks for 3D object recognition. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China (2017)","key":"36_CR14","DOI":"10.5244\/C.31.148"},{"doi-asserted-by":"crossref","unstructured":"Si, M., Tarnoczi, T.J., Wiens, B., Du, K.: Development of predictive emissions monitoring system using open source machine learning library \u2013 Keras: a case study on a cogeneration. Tetra Tech Canada Inc., Calgary, Canada (2019)","key":"36_CR15","DOI":"10.1109\/ACCESS.2019.2930555"},{"issue":"15","key":"36_CR16","doi-asserted-by":"publisher","first-page":"3371","DOI":"10.3390\/s19153371","volume":"19","author":"S Hossain","year":"2019","unstructured":"Hossain, S., Lee, D.J.: Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors 19(15), 3371 (2019)","journal-title":"Sensors"}],"container-title":["Advances in Intelligent Systems and Computing","Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-44289-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T14:17:34Z","timestamp":1666189054000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-44289-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030442880","9783030442897"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-44289-7_36","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"24 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}