{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:12:53Z","timestamp":1742922773259,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819914715"},{"type":"electronic","value":"9789819914722"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-1472-2_6","type":"book-chapter","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T03:37:55Z","timestamp":1687491475000},"page":"61-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward More Robust Multiclass Aerial Solar Panel Detection and Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9539-6719","authenticated-orcid":false,"given":"Indrajit","family":"Kar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7932-5307","authenticated-orcid":false,"given":"Sudipta","family":"Mukhopadhyay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6695-1632","authenticated-orcid":false,"given":"Bijon","family":"Guha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,23]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Lorenzoni A.: The support schemes for the growth of renewable energy (2010)","DOI":"10.2139\/ssrn.1618314"},{"key":"6_CR2","unstructured":"bloomberg.: Transition in energy, transport\u2014predictions for 2019 (2019)"},{"issue":"4","key":"6_CR3","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.3390\/app11041835","volume":"11","author":"KC Liao","year":"2021","unstructured":"Liao, K.C., Lu, J.H.: Using UAV to detect solar module fault conditions of a solar power farm with ir and visual image analysis. Appl. Sci. 11(4), 1835 (2021)","journal-title":"Appl. Sci."},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.rser.2018.05.027","volume":"93","author":"S Gallardo-Saavedra","year":"2018","unstructured":"Gallardo-Saavedra, S., Hern\u00e1ndez-Callejo, L., Duque-Perez, O.: Technological review of the instrumentation used in aerial thermographic inspection of photovoltaic plants. Renew. Sustain. Energy Rev. 93, 566\u2013579 (2018)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Almalki, F.A., Albraikan, A.A., Soufiene, B.O., Ali, O.: Utilizing Artificial intelligence and lotus effect in an emerging intelligent drone for persevering solar panel efficiency. Wirel. Commun. Mobile Comput. (2022)","DOI":"10.1155\/2022\/7741535"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., Vasconcelos N.: A unified multi-scale deep convolutional neural network for fast object detection. In ECCV. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He K, Hariharan B, Belongie, S.: Feature pyramid networks for object detection. In CVPR (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In ECCV. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"1","key":"6_CR9","first-page":"265","volume":"28","author":"G Cheng","year":"2018","unstructured":"Cheng, G., Han, J., Zhou, P., Xu, D.: Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection. IEEE TIP 28(1), 265\u2013278 (2018)","journal-title":"IEEE TIP"},{"issue":"12","key":"6_CR10","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","volume":"54","author":"G Cheng","year":"2016","unstructured":"Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405\u20137415 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Ye, Q., Qiu, Q, Jiao, J.: Oriented response networks. In: CVPR, pp. 4961\u20134970. IEEE (2017)","DOI":"10.1109\/CVPR.2017.527"},{"key":"6_CR12","unstructured":"Parhar, P., Sawasaki, R., Todeschini, A., Vahabi, H., Nusaputra, N., Vergara, F.: HyperionSolarNet: solar panel detection from aerial images. arXiv preprint arXiv:2201.02107 (2022)"},{"issue":"12","key":"6_CR13","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.1016\/j.joule.2018.11.021","volume":"2","author":"J Yu","year":"2018","unstructured":"Yu, J., Wang, Z., Majumdar, A., Rajagopal, R.: DeepSolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule 2(12), 2605\u20132617 (2018)","journal-title":"Joule"},{"key":"6_CR14","unstructured":"Camilo, J., Wang, R., Collins, L.M., Bradbury, K., Malof, J.M.: Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery. arXiv preprint arXiv:1801.04018 (2018)"},{"key":"6_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106283","volume":"92","author":"L Zhuang","year":"2020","unstructured":"Zhuang, L., Zhang, Z., Wang, L.: The automatic segmentation of residential solar panels based on satellite images: a cross learning driven U-Net method. Appl. Soft Comput. 92, 106283 (2020)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"6_CR16","first-page":"31","volume":"11","author":"MA Wani","year":"2021","unstructured":"Wani, M.A., Mujtaba, T.: Segmentation of satellite images of solar panels using fast deep learning model. Int. J. Renew. Energy Res. (IJRER) 11(1), 31\u201345 (2021)","journal-title":"Int. J. Renew. Energy Res. (IJRER)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Golovko, V., Kroshchanka, A., Mikhno, E., Komar, M., Sachenko, A.: Deep convolutional neural network for detection of solar panels. In: Data-Centric Business and Applications, pp. 371\u2013389. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-43070-2_17"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S. et al.: Speed\/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of CVPR, pp. 7310\u20137319 (2017)","DOI":"10.1109\/CVPR.2017.351"},{"key":"6_CR19","unstructured":"Jiang, Z., Zhao, L., Li, S., Jia, Y.: Real-time object detection method based on improved YOLOv4-tiny. arXiv preprint arXiv:2011.04244 (2020)"},{"key":"6_CR20","unstructured":"Cao, G., Xie, X., Yang, W., Liao, Q., Shi, G., Wu, J.: Feature-fused SSD: Fast detection for small objects. In: Ninth international conference on graphic and image processing (ICGIP 2017), vol. 10615, pp. 381\u2013388. SPIE (2018)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Sanjay, N.S., Ahmadinia, A.: MobileNet-Tiny: a deep neural network-based real-time object detection for rasberry Pi. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 647\u2013652. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00118"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Cheng, M., Bai, J., Li, L., Chen, Q., Zhou, X., Zhang, H., Zhang, P.: Tiny-RetinaNet: a one-stage detector for real-time object detection. In: Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), vol. 11373, pp. 195\u2013202. SPIE (2020)","DOI":"10.1117\/12.2557264"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Sumit, S.S., Watada, J., Roy, A., Rambli, D.R.A.: In object detection deep learning methods, YOLO shows supremum to Mask R-CNN. J. Phys. Conf. Ser. 1529(4), 042086 (2020). IOP Publishing","DOI":"10.1088\/1742-6596\/1529\/4\/042086"},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"89278","DOI":"10.1109\/ACCESS.2019.2925561","volume":"7","author":"J Yang","year":"2019","unstructured":"Yang, J., Li, S., Wang, Z., Yang, G.: Real-time tiny part defect detection system in manufacturing using deep learning. IEEE Access 7, 89278\u201389291 (2019)","journal-title":"IEEE Access"},{"issue":"6","key":"6_CR25","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1007\/s11036-021-01845-y","volume":"26","author":"X Xu","year":"2021","unstructured":"Xu, X., Liang, W., Zhao, J., Gao, H.: Tiny FCOS: A lightweight anchor-free object detection algorithm for mobile scenarios. Mobile Netw. Appl. 26(6), 2219\u20132229 (2021)","journal-title":"Mobile Netw. Appl."},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, W., Guo, H., Zhang, R., Xia, G.S.: Tiny object detection in aerial images. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3791\u20133798. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9413340"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Yang, L., Rakin, A.S., Fan, D.: Rep-Net: efficient on-device learning via feature reprogramming. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12277\u201312286 (2022)","DOI":"10.1109\/CVPR52688.2022.01196"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision (ECCV), pp. 734\u2013750 (2018)","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., Cho, K.: Augmentation for small object detection. arXiv preprint arXiv:1902.07296 (2019)","DOI":"10.5121\/csit.2019.91713"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Tong, K., Wu, Y.: Deep learning-based detection from the perspective of small or tiny objects: a survey. Image Vis. Comput., 104471 (2022)","DOI":"10.1016\/j.imavis.2022.104471"},{"key":"6_CR31","first-page":"1","volume":"60","author":"Y Yu","year":"2020","unstructured":"Yu, Y., Yang, X., Li, J., Gao, X.: A cascade rotated anchor-aided detector for ship detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"21","key":"6_CR32","doi-asserted-by":"publisher","first-page":"2506","DOI":"10.3390\/rs11212506","volume":"11","author":"X Xiao","year":"2019","unstructured":"Xiao, X., Zhou, Z., Wang, B., Li, L., Miao, L.: Ship detection under complex backgrounds based on accurate rotated anchor boxes from paired semantic segmentation. Remote Sens. 11(21), 2506 (2019)","journal-title":"Remote Sens."},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Koo, J., Seo, J., Jeon, S., Choe, J., Jeon, T.: RBox-CNN: Rotated bounding box based CNN for ship detection in remote sensing image. In: Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 420\u2013423 (2018)","DOI":"10.1145\/3274895.3274915"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Li, M., Guo, W., Zhang, Z., Yu, W., Zhang, T.: Rotated region based fully convolutional network for ship detection. In: IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, pp. 673\u2013676. IEEE (2018)","DOI":"10.1109\/IGARSS.2018.8519094"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, J., Weng, L., Yang, Y.: Rotated region based CNN for ship detection. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 900\u2013904. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296411"},{"issue":"11","key":"6_CR36","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.3390\/rs14112605","volume":"14","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Yu, C.: Point RCNN: an angle-free framework for rotated object detection. Remote Sens. 14(11), 2605 (2022)","journal-title":"Remote Sens."},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Azimi, S.M., Vig, E., Bahmanyar, R., K\u00f6rner, M., Reinartz, P.: Towards multi-class object detection in unconstrained remote sensing imagery. In: Asian Conference on Computer Vision, pp. 150\u2013165. Springer, Cham (2018)","DOI":"10.1007\/978-3-030-20893-6_10"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Deshmukh, S., Moh, T.S.: Fine object detection in automated solar panel layout generation. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1402\u20131407. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00228"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., Metaxas, D.: Oriented object detection in aerial images with box boundary-aware vectors. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2150\u20132159 (2021)","DOI":"10.1109\/WACV48630.2021.00220"}],"container-title":["Advances in Intelligent Systems and Computing","Recent Trends in Intelligence Enabled Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1472-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T03:41:29Z","timestamp":1687491689000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1472-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819914715","9789819914722"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1472-2_6","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DoSIER","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Doctoral Symposium on Intelligence Enabled Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cooch Behar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dosier2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dosier.drsiddhartha.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}