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Therefore, it is necessary to use efficient methodology addressing the challenges. Vehicle logo recognition (VLR) is a significant application in ITS. VLR is difficult due to the geometric distortions as well as various imaging situations simultaneously. However, traditional methods and hand-crafted features have many limitations. Convolutional neural network (CNN) enjoys the success in many machine vision tasks. Inspired by the excellent performance of CNN, we design and develop a novel VLR distributed system framework based on Hadoop ecosystem and deeplearning. We propose a Mapreduce based CNN called MRCNN to train the networks, which significantly increases the training speed and reduces the computation cost simultaneously. Furthermore, unlike previous classical CNN starting from a random initialization, we propose a novel genetic algorithm (GA) global optimization and Bayesian regularization approach called GABR in order to initialize the weights of classifier, which help prevent the overfitting and avoid the local optima. Compared with other algorithms, the proposed method performs best and increases the recognition accuracy with good initial weights optimized by GABR. The results show that the distributed system framework and proposed algorithms are suitable for real-world applications of VLR.<\/jats:p>","DOI":"10.3233\/jifs-17592","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T12:28:54Z","timestamp":1521808134000},"page":"1985-1994","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Effective vehicle logo recognition in real-world application using mapreduce based convolutional neural networks with a pre-training strategy"],"prefix":"10.1177","volume":"34","author":[{"given":"Binquan","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, HaiDian District, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Hu","sequence":"additional","affiliation":[{"name":"The Institute of Software, Chinese Academy of Sciences, Zhong Guan Cun, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,3,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","article-title":"Big data and deep learning: Challenges and perspectives","volume":"2","author":"Chen X.W.","year":"2014","unstructured":"ChenX.W. and LinX.G., Big data and deep learning: Challenges and perspectives, IEEE Access 2 (2014), 514\u2013525.","journal-title":"IEEE Access"},{"issue":"1","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","article-title":"Context-dependent pretrained deep neural networks for large-vocabulary speech recognition","volume":"20","author":"Dahl G.E.","year":"2012","unstructured":"DahlG.E., YuD., DengL. and AceroA., Context-dependent pretrained deep neural networks for large-vocabulary speech recognition, IEEE Transactions on Audio, Speech, and Language Processing 20(1) (2012), 30\u201341.","journal-title":"IEEE Transactions on Audio, Speech, and Language Processing"},{"issue":"12","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1162\/NECO_a_00052","article-title":"Deep, big, simple neural nets for handwritten digit recognition","volume":"22","author":"Cirean D.","year":"2010","unstructured":"CireanD., MelerU., CambardellaL. and SchmidhuberJ., Deep, big, simple neural nets for handwritten digit recognition, Neural Computation 22(12) (2010), 3207\u20133220.","journal-title":"Neural Computation"},{"key":"e_1_3_2_5_2","first-page":"873","volume-title":"Large-scale deep unsupervised learning using graphics processors","author":"Raina R.","year":"2009","unstructured":"RainaR., MadhavanA. and NgA., Large-scale deep unsupervised learning using graphics processors, In Proceeding of 26th International Conference on Machine Learning (2009), 873\u2013880."},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/ACCESS.2014.2319813","article-title":"Large-scale deep belief nets with MapReduce","volume":"2","author":"Zhang K.","year":"2014","unstructured":"ZhangK. and ChenX., Large-scale deep belief nets with MapReduce, IEEE Access 2 (2014), 395\u2013403.","journal-title":"IEEE Access"},{"key":"e_1_3_2_7_2","first-page":"5880","article-title":"Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering, In 2016 IEEE International Conference on Acoustics","author":"Chen K.","year":"2016","unstructured":"ChenK. and HuoQ., Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering, In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (2016), 5880\u20135884.","journal-title":"Speech and Signal Processing"},{"key":"e_1_3_2_8_2","unstructured":"GuptaS. 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