Name
Title | ||
|---|---|---|
Effective vehicle logo recognition in real-world application using mapreduce based convolutional neural networks with a pre-training strategy. |
Abstract | ||
|---|---|---|
Large amounts of data are generated by the intelligent transportation system (ITS) everyday. It exceeds the storage and processing capacity of conventional systems, and also doesn't fit the structures of current database. 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/JIFS-17592 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
MRCNN,vehicle logo recognition (VLR),Hadoop ecosystem,GA optimization,intelligent transportation system (ITS) | Convolutional neural network,Artificial intelligence,Logo recognition,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 3 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binquan Li | 1 | 0 | 0.34 |
| Xiaohui Hu | 2 | 17 | 8.10 |