Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery
Authors: Yecheng Huang, Jiaxin Chen, Di Huang1026-1033
AAAI 2022 | Venue PDF | 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. |