# SimD3: A Synthetic drone Dataset with Payload and Bird Distractor Modeling for Robust Detection

arXiv: https://arxiv.org/abs/2601.14742 · HTML: https://mlsanity.com/paper/2601.14742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [A Deep Learning Approach to Drone Monitoring](https://mlsanity.com/paper/1712.00863/) (2017)
- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [A dataset for multi-sensor drone detection](https://mlsanity.com/paper/2111.01888/) (2021)
- [Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks](https://mlsanity.com/paper/1812.08333/) (2018)
- [Cosys-AirSim: A Real-Time Simulation Framework Expanded for Complex Industrial Applications](https://mlsanity.com/paper/2303.13381/) (2023)
- [SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection](https://mlsanity.com/paper/2411.05633/) (2024)
- [YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs](https://mlsanity.com/paper/2512.18046/) (2025)

## Abstract (arXiv)

Reliable drone detection is challenging due to limited annotated real-world data, large appearance variability, and the presence of visually similar distractors such as birds. To address these challenges, this paper introduces SimD3, a large-scale high-fidelity synthetic dataset designed for robust drone detection in complex aerial environments. Unlike existing synthetic drone datasets, SimD3 explicitly models drones with heterogeneous payloads, incorporates multiple bird species as realistic distractors, and leverages diverse Unreal Engine 5 environments with controlled weather, lighting, and flight trajectories captured using a 360 six-camera rig. Using SimD3, we conduct an extensive experimental evaluation within the YOLOv5 detection framework, including an attention-enhanced variant termed Yolov5m+C3b, where standard bottleneck-based C3 blocks are replaced with C3b modules. Models are evaluated on synthetic data, combined synthetic and real data, and multiple unseen real-world benchmarks to assess robustness and generalization. Experimental results show that SimD3 provides effective supervision for small-object drone detection and that Yolov5m+C3b consistently outperforms the baseline across in-domain and cross-dataset evaluations. These findings highlight the utility of SimD3 for training and benchmarking robust drone detection models under diverse and challenging conditions.