.png){kind=icon}
返回
A vehicle classification model based on deep active learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2023.05.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Vehicle classification is widely used in intelligent transportation and smart cities. However, the good performance of vehicle classification often depends on a large number of labeling data, which leads to the high cost of manual labeling. How to use a small number of labeled samples with rich features to achieve good classification performance is a challenge in vehicle classification. To solve these prob-lems, we propose a deep active learning framework called Feature Fusion Spatial Pyramid Pooling and Re-parameterization Visual Geometry Group (FFSPP-RepVGG). The framework is mainly divided into two parts: query strategy and feature extraction module. Specifically, the query strategy uses Feature Fusion (FF) to calculate the loss, thus defining uncertainty and being able to select more valuable samples for labeling training. The feature extraction module uses Spatial Pyramid Pooling and Re-parameterization Visual Geometry Group (SPP-RepVGG) model, which can extract more reliable image features. It can re-duce the cost of data labeling and have better performance at the same time. The experimental results on the BIT-Vehicle data set and the car-10 data set show that the FFSPP-RepVGG framework has superior performance than that of the comparison models. (c) 2023 Published by Elsevier B.V.
Keyword:
RECOGNITION
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network基于半监督卷积神经网络的车辆类型分类
PREAI
分享收藏
Vehicle detection using improved region convolution neural network for accident prevention in smart roads基于改进区域卷积神经网络的智能道路交通事故预防车辆检测
OAAI
OAAI分享收藏分享收藏Multi-way backpropagation networks for training compact deep neural networks.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
查看更多内容