{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T19:55:35Z","timestamp":1714679735903},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"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":["Soft Comput"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00500-021-06706-0","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T12:02:41Z","timestamp":1641297761000},"page":"7641-7652","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Design of robust deep learning-based object detection and classification model for autonomous driving applications"],"prefix":"10.1007","volume":"26","author":[{"given":"Mesfer","family":"Al Duhayyim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahd N.","family":"Al-Wesabi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anwer Mustafa","family":"Hilal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manar Ahmed","family":"Hamza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shalini","family":"Goel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashish","family":"Khanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"issue":"6","key":"6706_CR2","doi-asserted-by":"publisher","first-page":"e275","DOI":"10.1016\/j.jvoice.2010.08.003","volume":"25","author":"MK Arjmandi","year":"2011","unstructured":"Arjmandi MK, Pooyan M, Mikaili M, Vali M, Moqarehzadeh A (2011) Identification of voice disorders using long-time features and support vector machine with different feature reduction methods. J Voice 25(6):e275\u2013e289","journal-title":"J Voice"},{"key":"6706_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3096854","author":"D Feng","year":"2021","unstructured":"Feng D, Harakeh A, Waslander SL, Dietmayer K (2021) A review and comparative study on probabilistic object detection in autonomous driving. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2021.3096854","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"6706_CR4","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.iatssr.2019.11.008","volume":"43","author":"H Fujiyoshi","year":"2019","unstructured":"Fujiyoshi H, Hirakawa T, Yamashita T (2019) Deep learning-based image recognition for autonomous driving. IATSS Research 43(4):244\u2013252","journal-title":"IATSS Research"},{"issue":"01","key":"6706_CR5","doi-asserted-by":"publisher","first-page":"1751001","DOI":"10.1142\/S0218001417510016","volume":"31","author":"K Hu","year":"2017","unstructured":"Hu K, Zhou Z, Weng L, Liu J, Wang L, Su Y, Yang Y (2017) An optimization strategy for weighted extreme learning machine based on PSO. Int J Pattern Recognit Artif Intell 31(01):1751001","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"6706_CR1","unstructured":"https:\/\/www.kaggle.com\/klemenko\/kitti-dataset"},{"issue":"5","key":"6706_CR6","doi-asserted-by":"publisher","first-page":"3681","DOI":"10.1109\/JIOT.2020.2967788","volume":"7","author":"X Jiang","year":"2020","unstructured":"Jiang X, Yu FR, Song T, Ma Z, Song Y, Zhu D (2020) Blockchain-enabled cross-domain object detection for autonomous driving: A model sharing approach. IEEE Internet Things J 7(5):3681\u20133692","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"6706_CR7","doi-asserted-by":"publisher","first-page":"424","DOI":"10.3390\/electronics10040424","volume":"10","author":"V John","year":"2021","unstructured":"John V, Mita S (2021) Deep feature-level sensor fusion using skip connections for real-time object detection in autonomous driving. Electronics 10(4):424","journal-title":"Electronics"},{"issue":"2","key":"6706_CR8","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/LRA.2021.3052442","volume":"6","author":"Y Liu","year":"2021","unstructured":"Liu Y, Yixuan Y, Liu M (2021) Ground-aware monocular 3d object detection for autonomous driving. IEEE Robot Autom Lett 6(2):919\u2013926","journal-title":"IEEE Robot Autom Lett"},{"key":"6706_CR9","doi-asserted-by":"crossref","unstructured":"Li P, Chen X, Shen S (2019) Stereo r-cnn based 3d object detection for autonomous driving. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u00a0(pp. 7644\u20137652)","DOI":"10.1109\/CVPR.2019.00783"},{"key":"6706_CR10","doi-asserted-by":"publisher","first-page":"194228","DOI":"10.1109\/ACCESS.2020.3033289","volume":"8","author":"Y Li","year":"2020","unstructured":"Li Y, Wang H, Dang LM, Nguyen TN, Han D, Lee A, Jang I, Moon H (2020) A deep learning-based hybrid framework for object detection and recognition in autonomous driving. IEEE Access 8:194228\u2013194239","journal-title":"IEEE Access"},{"key":"6706_CR11","doi-asserted-by":"crossref","unstructured":"Mohapatra S, Yogamani S, Gotzig H, Milz S, Mader P (2021) BEVDetNet: bird's eye view LiDAR point cloud based real-time 3D object detection for autonomous driving.\u00a0arXiv preprint arXiv: 2104.10780","DOI":"10.1109\/ITSC48978.2021.9564490"},{"key":"6706_CR12","doi-asserted-by":"crossref","unstructured":"Munir F, Azam S, Jeon M (2021) SSTN: self-supervised domain adaptation thermal object detection for autonomous driving.\u00a0arXiv preprint arXiv:2103.03150","DOI":"10.1109\/IROS51168.2021.9636353"},{"key":"6706_CR13","doi-asserted-by":"crossref","unstructured":"Nabati R, Qi H (2019) Rrpn: Radar region proposal network for object detection in autonomous vehicles. In\u00a02019 IEEE International Conference on Image Processing (ICIP)\u00a0(pp. 3093\u20133097). IEEE","DOI":"10.1109\/ICIP.2019.8803392"},{"key":"6706_CR14","first-page":"110","volume":"53","author":"CC Pham","year":"2017","unstructured":"Pham CC, Jeon JW (2017) Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks. Signal Process: Image Commun 53:110\u2013122","journal-title":"Signal Process: Image Commun"},{"key":"6706_CR15","doi-asserted-by":"crossref","unstructured":"Qian R, Lai X, Li X (2021) 3D object detection for autonomous driving: a survey.\u00a0arXiv preprint arXiv: 2106.10823.","DOI":"10.1016\/j.patcog.2022.108796"},{"key":"6706_CR16","doi-asserted-by":"crossref","unstructured":"Rashed H, Sallab AE, Yogamani S (2021) VM-MODNet: vehicle motion aware moving object detection for autonomous driving.\u00a0arXiv preprint arXiv : 2104.10985","DOI":"10.1109\/ITSC48978.2021.9564662"},{"issue":"8","key":"6706_CR17","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/s21082852","volume":"21","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ (2021) Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8):2852","journal-title":"Sensors"},{"issue":"9","key":"6706_CR18","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1177\/0037549717709932","volume":"93","author":"A U\u00e7ar","year":"2017","unstructured":"U\u00e7ar A, Demir Y, G\u00fczeli\u015f C (2017) Object recognition and detection with deep learning for autonomous driving applications. Simulation 93(9):759\u2013769","journal-title":"Simulation"},{"key":"6706_CR19","doi-asserted-by":"publisher","first-page":"18840","DOI":"10.1109\/ACCESS.2019.2897283","volume":"7","author":"G Wang","year":"2019","unstructured":"Wang G, Guo J, Chen Y, Li Y, Xu Q (2019) A PSO and BFO-based learning strategy applied to faster R-CNN for object detection in autonomous driving. IEEE Access 7:18840\u201318859","journal-title":"IEEE Access"},{"key":"6706_CR20","doi-asserted-by":"crossref","unstructured":"Wang J, Lan S, Gao M, Davis LS (2020) Infofocus: 3d object detection for autonomous driving with dynamic information modeling. In\u00a0European Conference on Computer Vision\u00a0(pp. 405\u2013420). Springer, Cham","DOI":"10.1007\/978-3-030-58607-2_24"},{"key":"6706_CR21","doi-asserted-by":"crossref","unstructured":"Wu B, Iandola F, Jin PH, Keutzer K (2017) Squeezedet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition workshops\u00a0(pp. 129\u2013137)","DOI":"10.1109\/CVPRW.2017.60"},{"key":"6706_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Z (2018) Improved adam optimizer for deep neural networks. In\u00a02018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS)\u00a0(pp. 1\u20132). IEEE","DOI":"10.1109\/IWQoS.2018.8624183"},{"issue":"10","key":"6706_CR23","first-page":"4193","volume":"8","author":"H Zheng","year":"2012","unstructured":"Zheng H, Zhou Y (2012) A novel cuckoo search optimization algorithm based on Gauss distribution. J Comput Inform Syst 8(10):4193\u20134200","journal-title":"J Comput Inform Syst"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06706-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-06706-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06706-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T13:14:26Z","timestamp":1657890866000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-06706-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,4]]},"references-count":23,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["6706"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-06706-0","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,4]]},"assertion":[{"value":"20 December 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}