A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets
Authors
Keywords
Traffic sign recognition, Convolutional neural network, Domain transfer learning, Genetic algorithms, Ternary crossover
Journal
NEURAL PROCESSING LETTERS
Volume -, Issue -, Pages -
Publisher
Springer Nature
Online
2019-02-05
DOI
10.1007/s11063-019-09991-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep detection network for real-life traffic sign in vehicular networks
- (2018) Tingting Yang et al. Computer Networks
- Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods
- (2018) Álvaro Arcos-García et al. NEURAL NETWORKS
- Airport Detection on Optical Satellite Images Using Deep Convolutional Neural Networks
- (2017) Peng Zhang et al. IEEE Geoscience and Remote Sensing Letters
- Traffic sign recognition based on color, shape, and pictogram classification using support vector machines
- (2017) Ahmed Madani et al. NEURAL COMPUTING & APPLICATIONS
- Identification of rice diseases using deep convolutional neural networks
- (2017) Yang Lu et al. NEUROCOMPUTING
- A survey of deep neural network architectures and their applications
- (2017) Weibo Liu et al. NEUROCOMPUTING
- Mathematical analysis of schema survival for genetic algorithms having dual mutation
- (2017) Apoorva Mishra et al. SOFT COMPUTING
- Mathematical analysis of the cumulative effect of novel ternary crossover operator and mutation on probability of survival of a schema
- (2017) Apoorva Mishra et al. THEORETICAL COMPUTER SCIENCE
- Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition
- (2017) Jinpeng Li et al. Cognitive Computation
- An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine
- (2017) Zhiyong Huang et al. IEEE Transactions on Cybernetics
- Fast Branch Convolutional Neural Network for Traffic Sign Recognition
- (2017) Wenzheng Hu et al. IEEE Intelligent Transportation Systems Magazine
- A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles
- (2016) Eftychios Protopapadakis et al. COMPUTERS & STRUCTURES
- A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems
- (2016) Zhang Qiongbing et al. EXPERT SYSTEMS WITH APPLICATIONS
- Towards Real-Time Traffic Sign Detection and Classification
- (2016) Yi Yang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data
- (2016) Yongtao Yu et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A genetic-based effective approach to path-planning of autonomous underwater glider with upstream-current avoidance in variable oceans
- (2016) Chien-Chou Shih et al. SOFT COMPUTING
- Adaptive-mutation compact genetic algorithm for dynamic environments
- (2016) Chigozirim J. Uzor et al. SOFT COMPUTING
- A genetic algorithm for the maximum edge-disjoint paths problem
- (2015) Chia-Chun Hsu et al. NEUROCOMPUTING
- A modified genetic algorithm applied to the elevator dispatching problem
- (2015) M. Beamurgia et al. SOFT COMPUTING
- A novel probabilistically-guided context-sensitive crossover operator for clustering
- (2013) Amit Banerjee Swarm and Evolutionary Computation
- Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition
- (2012) Ke Lu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started