Learning Transferable UAV for Forest Visual Perception
Authors: Lyujie Chen, Wufan Wang, Jihong Zhu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation and real-world flight with a variety of appearance and environment changes are both tested. The Res Net-18 adaptation and its variant model achieve the best result of 84.08% accuracy in reality. |
| Researcher Affiliation | Academia | Lyujie Chen, Wufan Wang, Jihong Zhu Beijing National Research Center for Information Science and Technology (BNRist) Department of Computer Science and Technology, Tsinghua University, Beijing, China {chenlj16, wwf14, jhzhu}@mails.tsinghua.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'Additional videos and the full training/testing datasets are available at https://sites.google.com/view/forest-trail-dataset.' This link refers to datasets and videos, but does not explicitly mention the source code for the methodology described in the paper. |
| Open Datasets | Yes | Additional videos and the full training/testing datasets are available at https://sites.google.com/view/forest-trail-dataset. |
| Dataset Splits | Yes | Task Training Data Source Number of Training Data Validation Data Source Number of Validation Data Test Data Source Number of Test Data |
| Hardware Specification | Yes | It requires about 5 hours on a server equipped with an NVIDIA Titan X GPU. |
| Software Dependencies | No | The model is implemented in Caffe [Jia et al., 2014] and trained using standard backpropagation. |
| Experiment Setup | Yes | The initial learning rate is set to be 0.05. It requires about 5 hours on a server equipped with an NVIDIA Titan X GPU. Then, we introduce the unlabeled data in target domain to train a Res Net-18 adaptation network. We set its learning rate to be 0.003 and use the SGD with 0.75 momentum. After every 300 iterations of training, a test will be conducted on validation set. |