{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:54:23Z","timestamp":1742928863303,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031180491"},{"type":"electronic","value":"9783031180507"}],"license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-18050-7_36","type":"book-chapter","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T19:02:55Z","timestamp":1665514975000},"page":"371-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of\u00a0Polymers Based on\u00a0the\u00a0Degree of\u00a0Their Transparency in\u00a0SWIR Spectrum"],"prefix":"10.1007","author":[{"given":"Dominik","family":"Stursa","sequence":"first","affiliation":[]},{"given":"Dusan","family":"Kopecky","sequence":"additional","affiliation":[]},{"given":"Jiri","family":"Rolecek","sequence":"additional","affiliation":[]},{"given":"Petr","family":"Dolezel","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Baruque Zanon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Serranti, S., Fiore, L., Bonifazi, G., Takeshima, A., Takeuchi, H., Kashiwada, S.: Microplastics characterization by hyperspectral imaging in the SWIR range, vol. 11197 (2019)","DOI":"10.1117\/12.2542793"},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Bonifazi, G., Fiore, L., Gasbarrone, R., Hennebert, P., Serranti, S.: Detection of brominated plastics from e-waste by short-wave infrared spectroscopy. Recycling 6(3), 54 (2021)","DOI":"10.3390\/recycling6030054"},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"42","DOI":"10.31025\/2611-4135\/2022.15168","volume":"18","author":"G Bonifazi","year":"2022","unstructured":"Bonifazi, G., Capobianco, G., Cucuzza, P., Serranti, S., Uzzo, A.: Recycling-oriented characterization of pet waste stream by SWIR hyperspectral imaging and variable selection methods. Detritus 18, 42\u201349 (2022)","journal-title":"Detritus"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Araujo-Andrade, C., et al.: Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling. Waste Manage. Res. 39(5), 631\u2013651 (2021)","DOI":"10.1177\/0734242X21997908"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Caballero, D., Bevilacqua, M., Amigo, J.M.: Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame retardants. J. Spectral Imaging 8 (2019)","DOI":"10.1255\/jsi.2019.a1"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Dolezel, P., Stursa, D., Kopecky, D., Jecha, J.: Memory efficient grasping point detection of nontrivial objects. IEEE Access 9, 82130\u201382145 (2021)","DOI":"10.1109\/ACCESS.2021.3086417"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Nguyen, N.-D., Do, T., Ngo, T.D., Le, D.-D.: An evaluation of deep learning methods for small object detection (2020)","DOI":"10.1155\/2020\/3189691"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Ju, M., Luo, H., Wang, Z., Hui, B., Chang, Z.: The application of improved yolo v3 in multi-scale target detection 9(18) (2019)","DOI":"10.3390\/app9183775"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"36_CR10","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection, vol. 2016-December, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Pi, Y., Nath, N.D., Behzadan, A.H.: Detection and semantic segmentation of disaster damage in UAV footage. J. Comput. Civil Eng. 35(2), 04020063 (2021)","DOI":"10.1061\/(ASCE)CP.1943-5487.0000947"},{"key":"36_CR13","unstructured":"HDR SWIR camera: Accessed 6 May 2022. https:\/\/new-imaging-technologies.com\/swir-products\/widy-swir\/"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"36_CR15","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, vol. 2016-December, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network, vol. 2017-January, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"36_CR18","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$0.5 mb model size (2016)"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Beheshti, N., Johnsson, L.: Squeeze U-Net: a memory and energy efficient image segmentation network. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1495\u20131504 (2020)","DOI":"10.1109\/CVPRW50498.2020.00190"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","DOI":"10.1016\/j.media.2019.01.012"}],"container-title":["Lecture Notes in Networks and Systems","17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18050-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T19:06:30Z","timestamp":1665515190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18050-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,12]]},"ISBN":["9783031180491","9783031180507"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18050-7_36","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022,10,12]]},"assertion":[{"value":"12 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SOCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Soft Computing Models in Industrial and Environmental Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socomoin2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2022.sococonference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}