UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery
Authors: Yecheng Huang, Jiaxin Chen, Di Huang1026-1033
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are carried out on the widely used Vis Drone and UAVDT datasets, and UFPMP-Det reports new state-of-the-art scores at a much higher speed, highlighting its advantages. |
| Researcher Affiliation | Academia | 1 State Key Laboratory of Software Development Environment, Beihang University, Beijing, China 2 School of Computer Science and Engineering, Beihang University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Foreground Region Generation |
| Open Source Code | Yes | The code is available at https://github.com/PuAnysh/UFPMP-Det. |
| Open Datasets | Yes | UFPMP-Det is evaluated on the widely-used Vis Drone (Zhu et al. 2018) and UAVDT (Du et al. 2018) benchmarks and extensive experiments are carried out. |
| Dataset Splits | Yes | Vis Drone consists of 10,209 high resolution images (2000 × 1500) with 10 object categories... 6,471 images are used for training, 548 for validation and 3,190 for test. |
| Hardware Specification | Yes | All the experiments are conducted on one GTX 1080TI GPU. |
| Software Dependencies | No | The paper mentions the use of the "MMDetection toolbox" and "GFL" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The momentum and weight decay are fixed as 0.9 and 0.0001, respectively. The initial learning rate is set at 0.01 with a linear warm-up, which decreases by the factor of 10 after 40 and 55 epochs. |