FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection
Authors: Yao Xiao, Tingfa Xu, Yu Xin, Jianan Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on three major aerial image datasets, including Visdrone, UAVDT, and AI-TOD, demonstrate that FBRT-YOLO outperforms various real-time detectors in terms of performance and speed. Experiments Implementation Details We conduct extensive experiments on three object detection benchmarks based on aerial images, i.e. Visdrone, UAVDT, and AI-TOD. |
| Researcher Affiliation | Academia | Yao Xiao, Tingfa Xu*, Yu Xin, Jianan Li Beijing Institute of Technology EMAIL |
| Pseudocode | No | The paper describes the proposed modules (FCM and MKP) with mathematical formulations and architectural diagrams (e.g., Fig. 3), but it does not include a clearly labeled pseudocode or algorithm block with structured steps. |
| Open Source Code | Yes | Code https://github.com/galaxy-oss/FCM. |
| Open Datasets | Yes | Extensive experimental results on three major aerial image datasets, including Visdrone, UAVDT, and AI-TOD, demonstrate that FBRT-YOLO outperforms various real-time detectors in terms of performance and speed. Extensive experiments conducted on widely-used aerial image benchmarks such as Vis Drone (2018), UAVDT (2018), and AI-TOD (2021) demonstrate that our FBRT-YOLO significantly outperforms previous state-of-the-art YOLO series models. |
| Dataset Splits | No | The paper mentions using well-known benchmark datasets such as Visdrone, UAVDT, and AI-TOD, but it does not explicitly specify the training, validation, and test splits (e.g., percentages or exact counts) used for these datasets in the provided text. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA Ge Force RTX 4090 GPU, except that the inference speed is test on a single RTX 3080 GPU. |
| Software Dependencies | No | The paper mentions various model architectures like YOLOv8, YOLOv10, and RT-DETR, and training parameters like SGD optimizer, but it does not specify explicit version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Our network is trained for 300 epochs using the stochastic gradient descent (SGD) optimizer with a momentum of 0.937, a weight decay of 0.0005, a batch size of 4, and an initial learning rate of 0.01. |