Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

By Zhen Dong, Yuwei Wu, Mingtao Pei, Yunde Jia. IEEE transactions on intelligent transportation systems (Print), 2015

348 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/86154acd26c47469a2789eba67db96fb79532c37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Huaiyu Li
2. Huapeng Li
3. Zhe Dai
4. Xin Pan
5. Ce Zhang
6. Xu Yun
7. Peter M. Atkinson
8. Isabel Sargent

Citations in the corpus

• An object-based convolutional neural network (OCNN) for urban land use classification (2018, background): “…and scene understanding, the CNN has demonstrated great potential in 99 many remote sensing tasks such as vehicle detection (Chen et al., 2014; Dong et al., 2015), 100 road network extraction (Cheng et al., 2017), remotely sensed scene classification (Othman et 101 al., 2016; Sargent et al.,…”
• Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning (2020, neutral): “Vehicle detection aims to find the location of the vehicle and determine its category [39].”
• Vision-based vehicle detection and counting system using deep learning in highway scenes (2019, methodology): “The datasets are taken by surveillance cameras; an example is the BIT-Vehicle Dataset [35], which contains 9,850 images.”
• Joint Deep Learning for land cover and land use classification (2019, background): “…117 feature representation, the CNN has demonstrated great potential in many remotely sensed 118 tasks such as vehicle detection (Chen et al., 2014; Dong et al., 2015), road network extraction 119 (Cheng et al., 2017), remotely sensed scene classification (Othman et al., 2016), and semantic 120…”
• A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification (2017, methodology): “81 In the remote sensing domain, CNNs have been studied actively and shown to produce 82 state-of-the-art results over the past few years, focusing primarily on object detection 83 (Dong et al., 2015) or scene classification (Hu et al., 2015a; Zhang et al., 2016).”