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A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 5, p e0251339 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.
- Subjects :
- Computer science
Pooling
Predation
Transportation
computer.software_genre
Convolutional neural network
Pattern Recognition, Automated
Machine Learning
computer.programming_language
Mammals
Multidisciplinary
Ecology
Eukaryota
Robotics
Pascal (programming language)
Trophic Interactions
Insects
Community Ecology
Moths and Butterflies
Vertebrates
Physical Sciences
Medicine
Engineering and Technology
Research Article
Optimization
Computer and Information Sciences
Arthropoda
Science
Machine learning
Birds
Deep Learning
Artificial Intelligence
Classifier (linguistics)
Animals
Wolves
business.industry
Deep learning
Ecology and Environmental Sciences
Organisms
Biology and Life Sciences
Function (mathematics)
Invertebrates
Boats
Object detection
Expression (mathematics)
Amniotes
Neural Networks, Computer
Artificial intelligence
business
Zoology
Entomology
computer
Mathematics
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....4e3928bc19c2dd09ffd3f3e903572727