{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:15:07Z","timestamp":1742930107802,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819756773"},{"type":"electronic","value":"9789819756780"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5678-0_21","type":"book-chapter","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T16:04:18Z","timestamp":1722528258000},"page":"239-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Avoid Orientation Confusion in Symmetrical Oriented Object Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3788-8237","authenticated-orcid":false,"given":"Ruoxin","family":"Liang","sequence":"first","affiliation":[]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Desheng","family":"Han","sequence":"additional","affiliation":[]},{"given":"Jianbing","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Yu, Y., Da, F.: Phase-shifting coder: predicting accurate orientation in oriented object detection. arXiv preprint arXiv:2211.06368 (2023)","DOI":"10.1109\/CVPR52729.2023.01283"},{"key":"21_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2957849","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Gao, H., Guo, L., Qin, A., Cai, C., You, Z.: A data-flow oriented deep ensemble learning method for real-time surface defect inspection. IEEE Trans. Instrum. Meas. (2019). https:\/\/doi.org\/10.1109\/TIM.2019.2957849","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3193183","volume":"71","author":"H Wu","year":"2022","unstructured":"Wu, H., Lei, R., Peng, Y.: PCBNet: a lightweight convolutional neural network for defect inspection in surface mount technology. IEEE Trans. Instrum. Meas. 71, 1\u201314 (2022). https:\/\/doi.org\/10.1109\/TIM.2022.3193183","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: AutoAlign: pixel-instance feature aggregation for multi-modal 3D object detection. arXiv preprint arXiv:2201.06493 (2022)","DOI":"10.24963\/ijcai.2022\/116"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. arXiv preprint arXiv:1903.11027 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. arXiv preprint arXiv:1612.03144 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"21_CR8","unstructured":"Zhao, Z.-Q., Zheng, P., Xu, S., Wu, X.: Object detection with deep learning: a review. arXiv preprint arXiv:1807.05511 (2019)"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J.: On the arbitrary-oriented object detection: classification based approaches revisited. arXiv preprint arXiv:2003.05597 (2022)","DOI":"10.1007\/s11263-022-01593-w"},{"key":"21_CR10","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., Tian, Q.: Rethinking rotated object detection with Gaussian Wasserstein distance loss. arXiv preprint arXiv:2101.11952 (2022)"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Yin, T., Zhou, X., Kr\u00e4henb\u00fchl, P.: Center-based 3D object detection and tracking. arXiv preprint arXiv:2006.11275 (2021)","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Mao, J., Wang, X., Li, H.: Interpolated convolutional networks for 3D point cloud understanding. arXiv preprint arXiv:1908.04512 (2019)","DOI":"10.1109\/ICCV.2019.00166"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Mao, J., Shi, S., Wang, X., Li, H.: 3D Object detection for autonomous driving: a comprehensive survey. arXiv preprint arXiv:2206.09474 (2023)","DOI":"10.1007\/s11263-023-01790-1"},{"key":"21_CR14","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.3390\/s18103337","volume":"18","author":"Y Yan","year":"2018","unstructured":"Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors 18, 3337 (2018). https:\/\/doi.org\/10.3390\/s18103337","journal-title":"Sensors"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. arXiv preprint arXiv:1711.06396 (2017)","DOI":"10.1109\/CVPR.2018.00472"},{"key":"21_CR16","unstructured":"Zhu, B., Jiang, Z., Zhou, X., Li, Z., Yu, G.: Class-balanced grouping and sampling for point cloud 3D object detection. arXiv preprint arXiv:1908.09492 (2019)"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. arXiv preprint arXiv:1812.05784 (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561. (2016)","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"21_CR20","unstructured":"Qian, W., Yang, X., Peng, S., Guo, Y., Yan, J.: Learning modulated loss for rotated object detection. arXiv preprint arXiv:1911.08299 (2019)"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Yang, X., et al.: SCRDet: towards more robust detection for small, cluttered and rotated objects. arXiv preprint arXiv:1811.07126 (2019)","DOI":"10.1109\/ICCV.2019.00832"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., Yan, J.: Dense label encoding for boundary discontinuity free rotation detection. arXiv preprint arXiv:2011.09670 (2021)","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"21_CR23","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.optlaseng.2018.04.019","volume":"109","author":"C Zuo","year":"2018","unstructured":"Zuo, C., Feng, S., Huang, L., Tao, T., Yin, W., Chen, Q.: Phase shifting algorithms for fringe projection profilometry: a review. Opt. Lasers Eng. 109, 23\u201359 (2018). https:\/\/doi.org\/10.1016\/j.optlaseng.2018.04.019","journal-title":"Opt. Lasers Eng."},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. arXiv preprint arXiv:1708.02002 (2018)","DOI":"10.1109\/ICCV.2017.324"},{"key":"21_CR25","unstructured":"OpenPCDet Development Team. OpenPCDet: an open-source toolbox for 3D object detection from point clouds (2020). https:\/\/github.com\/open-mmlab\/OpenPCDet"},{"key":"21_CR26","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5678-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T16:33:26Z","timestamp":1722530006000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5678-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756773","9789819756780"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5678-0_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}