---
title: DUT Anti-UAV Dataset
url: https://www.emergentmind.com/topics/dut-anti-uav-dataset
type: topic
---

# DUT Anti-UAV Dataset

The DUT Anti-UAV dataset is a curated benchmark designed for computer vision-based UAV detection and tracking, addressing the growing demand for effective anti-UAV surveillance strategies in complex visual environments. Developed by Dalian University of Technology, the dataset comprises visible-light imagery and video sequences with precise manual annotations, facilitating rigorous evaluation of state-of-the-art detection and tracking algorithms. It is structured into dedicated detection and tracking subsets and is released under the MIT license, enabling broad academic and commercial usage [2205.10851].

## 1. Dataset Structure and Annotation Protocol

### Detection Subset
The detection set contains 10,000 images partitioned into training (5,200 images; 5,243 UAV annotations), validation (2,600; 2,621), and test (2,200; 2,245) splits. Each instance of the sole “UAV” class is labeled using an axis-aligned bounding box ($(x_{min}, y_{min}, w, h)$; units in pixels) and typically recorded as:

```
000123.jpg, x_min=345, y_min=120, w=48, h=30, class="uav"
```

Over 35 distinct UAV types are represented, spanning rotorcraft and fixed-wing models. Scene diversity includes urban (high-rises, parking lots), rural (fields, forests), varied weather (sunny, cloudy, snowy), and broad temporal coverage (dawn/dusk, day, night).

Quantitative image statistics:
- UAV bounding box area ratio: $1.9\times 10^{-6}$ (min) to $0.70$ (max); mean $\approx 1.3\%$
- Aspect ratio ($w/h$): $1.0$ to $6.67$; mean $\approx 1.9$
- UAV spatial distribution: centers denser near image centroid, otherwise uniform in $x$/$y$

### Tracking Subset
Composed of 20 fully annotated video sequences (total $\approx 24,\!804$ frames; $\approx 1,\!240$ frames/sequence), these span both short-term ($<$500 frames) and long-term ($>$2,000 frames) tracking scenarios. Each frame contains a single axis-aligned bounding box. Video resolutions are $720\times 1280$ or $1080\times 1920$.

Key dynamics:
- UAV scale (box/image area): per-frame ratio $2.7\times 10^{-4}$ to $4.5\%$
- Aspect ratio per-frame: $1.0$ to $4.0$ (notably, Video 10 varies $1.0\to 4.33$)
- Presented challenges: extremely small targets, rapid motion, camera shake, illumination variation, occlusion, and out-of-view events

## 2. Evaluation Protocols and Metrics

### Detection Evaluation
Standard object detection metrics are adopted:
- **Intersection over Union (IoU):** $\mathrm{IoU}(B_{gt}, B_{pred}) = \frac{|B_{gt} \cap B_{pred}|}{|B_{gt} \cup B_{pred}|}$
- **True/False Positives (TP/FP), False Negatives (FN):** TP if $\mathrm{IoU} \geq \tau$ and class match; FN if GT box unmatched
- **Precision and Recall:** $P = \frac{TP}{TP + FP},\quad R = \frac{TP}{TP + FN}$
- **F₁-score:** $\mathrm{F}_1 = 2\frac{P R}{P + R}$
- **Average Precision (AP):** $\mathrm{AP}_{\tau} = \int_0^1 P(R)\, dR$ at IoU threshold $\tau$
- **mean AP (mAP):** $\mathrm{mAP} = \frac{1}{K}\sum_{k=1}^K \mathrm{AP}_{\tau_k}$; $K=1, \tau_1=0.5$ in experiments

### Tracking Evaluation (One-Pass Evaluation, OPE)
Trackers are initialized with the ground-truth in frame 1, run to sequence end with no re-initialization:
- **Success plot (overlap):** Given $\mathrm{IoU}_t$ at frame $t$, rate at threshold $\delta$: $s(\delta) = (1/N)\cdot \#\{t:\mathrm{IoU}_t\geq\delta\}$
- **Overall success score:** Area under $s(\delta)$ over $\delta \in [0, 1]$: $\mathrm{Success}=\int_{0}^{1}s(\delta)\,d\delta$
- **Precision plot (center-error):** $d_t =$ Euclidean center distance; precision at error $\epsilon$: $P(\epsilon) = (1/N)\cdot \#\{t:d_t\leq\epsilon\}$
- **Normalized precision:** AUC of $P(\epsilon)$ for $\epsilon\in[0,50]$ px, divided by $50$

## 3. Benchmark Algorithm Results

### Detection Baselines

| Method           | Backbone   | mAP   | FPS  |
|------------------|-----------|:-----:|:----:|
| Faster-R-CNN     | ResNet-50 | 0.653 | 12.8 |
| Cascade-R-CNN    | ResNet-50 | 0.683 | 10.7 |
| ATSS             | ResNet-50 | 0.642 | 13.3 |
| SSD              | VGG16     | 0.632 | 33.2 |
| YOLOX            | ResNet-18 | 0.400 | 53.7 |

- Cascade-R-CNN delivers the highest mAP and recall across all precision levels.
- YOLOX achieves maximal inference speed at the expense of detection accuracy.
- Precision–Recall curves at IoU=0.5 and IoU=0.75 further substantiate method-specific trade-offs.

### Tracking Baselines

| Tracker   | Success | NormPre | Pre@20px |
|-----------|:-------:|:-------:|:--------:|
| SiamFC    | 0.381   | 0.526   | 0.623    |
| ECO       | 0.404   | 0.643   | 0.717    |
| SPLT      | 0.405   | 0.585   | 0.651    |
| ATOM      | 0.574   | 0.758   | 0.830    |
| SiamRPN++ | 0.545   | 0.709   | 0.780    |
| DiMP      | 0.578   | 0.756   | 0.831    |
| TransT    | 0.586   | 0.765   | 0.832    |
| LTMU      | 0.608   | 0.783   | 0.858    |

LTMU demonstrates the highest success and precision scores among tested tracking approaches.

### Detection-Fused Tracking

A detection-tracking fusion algorithm is specified: If tracker confidence $<\tau_t$ ($\tau_t=0.9$), re-detect and select the bounding box with higher confidence (subject to detector score $>\tau_d=0.9$). Fusing with Faster-R-CNN (VGG16) yields the largest per-tracker improvements, e.g., SiamFC: 0.381 $\to$ 0.617 (+0.236 success), with similar improvements in precision.

## 4. Comparison to Anti-UAV Multi-Modal Benchmark

The DUT Anti-UAV is distinct from the larger Anti-UAV dataset [2101.08466], which provides 318 unaligned RGB–IR video pairs and over 585,900 bounding boxes for advanced multi-modal tracking evaluation. While DUT is focused exclusively on visible-light imagery and offers single-class annotations, Anti-UAV emphasizes modality fusion and attribute-rich annotation (seven binary attributes per IR clip: Out-of-View, Occlusion, Fast Motion, Scale Variation, Low Illumination, Thermal Cross-Over, Low Resolution). Evaluative protocols, error definitions, and baseline results differ accordingly.

Both datasets share object-centered bounding box annotation. However, Anti-UAV expands the research landscape toward multi-modal (RGB/IR), long-term, and attribute-intensive UAV tracking. DUT’s smaller, more focused structure facilitates benchmarking fine-grained detection and tracking in visually challenging, single-modality scenes.

## 5. Dataset Distribution and Licensing

The DUT Anti-UAV dataset is accessible via [https://github.com/wangdongdut/DUT-Anti-UAV](https://github.com/wangdongdut/DUT-Anti-UAV), with the following structure:
- “detection” folder: Images and subfolder “Annotations” containing TXT/CSV files per image
- “tracking” folder: 20 subfolders, each with sequential frames (.jpg) and groundtruth.txt
- Example code for training/inference is provided under “scripts/”

The dataset is released under the MIT license, permitting unrestricted academic and commercial usage provided attribution is maintained [2205.10851].

## 6. Implications and Future Research Directions

A plausible implication is that the DUT Anti-UAV dataset enables systematic study of visual anti-UAV detection and tracking in scenarios featuring small targets, broad environmental variation, and significant target appearance diversity. Its focused annotation protocol and detection-tracking fusion methodology advance high-precision tracking. Future research may leverage DUT for benchmarking detection-fused tracking, exploiting multi-modal fusion (as explored in Anti-UAV [2101.08466]), and addressing challenges such as occlusion, scale variation, and out-of-view events via robust data augmentation or advanced model design.

The dataset’s open licensing and comprehensive structure support its use as a reference testbed for computer vision-based UAV surveillance systems, evaluation of detection/tracking architecture performance, and development of adaptive anti-UAV solutions.

Source: https://www.emergentmind.com/topics/dut-anti-uav-dataset