---
title: 'MONET Dataset: Drone Thermal Imaging'
url: https://www.emergentmind.com/topics/monet-dataset
type: topic
---

# MONET Dataset: Drone Thermal Imaging

Searching arXiv for the dataset paper and closely related work to support a precise article.
MONET, short for **Multimodal drOne thErmal daTaset**, is a **drone-borne long-wave infrared (LWIR) thermal dataset** recorded in **rural environments** for studying **object localisation** and **behaviour understanding** of **humans and vehicles** under moving-viewpoint aerial thermal imaging. It was introduced as a dataset of approximately **53K images** with **162K manually annotated bounding boxes**, where each image is **timestamp-aligned with drone metadata** including **attitudes, speed, altitude, and GPS coordinates**. MONET is designed around two rural sites with distinct structures and cluttered backgrounds, and emphasizes the combined difficulty of thermal sensing, moving cameras, large scale variation, and cross-site transfer [2304.05417].

## 1. Recording context and intended problem setting

MONET was recorded in a **rural area near the city of Nicosia, Cyprus**, in **mid-December**, during the **afternoon, evening, and night**. The dataset contains two main recording sites: **runway** and **dirt-road**. The runway was recorded near a runway on property of **The Cyprus Institute**, while the dirt-road scenario was recorded on **agricultural land**. The two sites were chosen because they have **different scene structures** and **different cluttered backgrounds**, making them suitable for transfer-learning and domain-shift studies [2304.05417].

The dataset targets aerial thermal perception under conditions that are explicitly difficult for standard detectors. The paper identifies several sources of difficulty: thermal cameras usually have **lower resolution** than RGB cameras; target appearance varies with **temperature, emissivity, ambient temperature, and humidity**; backgrounds can emit similar heat to targets; images can be affected by **noise and ghost effects**; and objects can be **small**, **occluded**, or **scale-varying** because of altitude and viewpoint changes. MONET further compounds these issues through a **moving drone** and **different and moving viewpoints**, rather than a static top-down platform. This makes it simultaneously a detection dataset, a tracking dataset, and a testbed for multimodal aerial reasoning.

The target categories are **person** and **vehicle**, with an additional **ignore** category used for a specific runway region. The dataset was explicitly constructed to support **object detection / localisation**, **multi-object tracking**, **trajectory analysis**, **behaviour/activity understanding**, **transfer learning / domain adaptation across sites**, and **multimodal learning** that exploits aligned flight metadata.

## 2. Sensor platform, image modality, and metadata alignment

MONET was collected using a **fully customised multirotor drone** developed for automated surveillance and archaeological looting detection. The platform has **eight motors** and **eight electronic speed controllers**, mounted on **four arms**, with payload capacity of **up to 1.5 kg**. Onboard sensing for flight control includes a **high-frequency IMU**, **barometric altimeter**, **external GPS**, and **compass module**. The platform supports manual and autonomous flight modes, including **hovering**, **automated waypoint navigation**, **return-to-home**, **auto take-off**, and **auto-landing** [2304.05417].

The acquisition system uses the **Workswell WIRIS Security** camera, which contains both an **RGB sensor** and a **thermal sensor**. The RGB sensor supports **Full HD \(1920\times1080\)** at **30 Hz** with up to **30× optical zoom**. The thermal sensor operates in the **LWIR** range of **\(7.5\text{–}13.5 \,\mu m\)** at **\(800\times600\)** resolution, with a thermal sensitivity range reported as **\(-20^\circ C\) to \(150^\circ C\)**. The camera is mounted on a **gimbal**, and MONET includes **gimbal attitude metadata** in addition to drone attitude metadata.

The software pipeline used the camera’s **proprietary SDK** over **Ethernet**, integrated into the drone ground station using **ROS**. RGB and thermal streams were transmitted as **RTSP video streams**, received by a **Data Processing Unit**, and published by ROS nodes. Image timestamps and metadata timestamps were both logged during acquisition. Metadata was captured at about **40 Hz on average**, and image-to-metadata alignment was performed by **nearest-neighbour assignment between image and metadata timestamps**. Per image, the aligned metadata fields include:

- **date** in **ISO 8601** format
- **drone pitch**, **drone roll**, **drone yaw**
- **gimbal pitch**, **gimbal roll**, **gimbal yaw**
- **latitude**, **longitude**
- **altitude**
- **x-axis speed**, **y-axis speed**, **z-axis speed**

Although the sensor package is intrinsically RGB-plus-thermal, the paper states that **RGB images are not included in the current release** because of unresolved privacy concerns. In practice, the released resource is therefore centered on **thermal imagery plus aligned drone metadata** [2304.05417].

## 3. Dataset composition, classes, and annotation protocol

MONET contains approximately **53K images / frames** and **162K manually annotated bounding boxes**. The paper reports the following site-wise totals:

| Site | Frames | Bounding boxes |
|---|---:|---:|
| dirt-road | 23.3K | 83.4K |
| runway | 29.4K | 79.3K |

The annotation categories are **person**, **vehicle**, and **ignore**. The **vehicle** category is defined as **car-like objects**. The **ignore** category marks a region near the hangar area in the runway scenario. The rationale is to prevent models from learning a contextual bias that people and vehicles often occur next to the hangar; pixels inside ignore regions were therefore **zeroed out** during training, while the original images remain available for alternative use [2304.05417].

Target density statistics are reported for frames that contain targets. For **person**, the average number of instances is **2.96**, with **max 6** and **min 1**. For **vehicle**, the average is **1.33**, with **max 4** and **min 1**. For **ignore**, the count is always **1** when present. Bounding boxes are linked with **target identities**, so the dataset supports **object trajectories** and **multi-object tracking**.

Annotations were created using **CVAT** by **six people**, with **three people** subsequently double-checking the labels for accuracy and consistency. The annotation rules are unusually explicit. Annotators were instructed to draw boxes **as tight as possible**, provided the object was clearly distinguishable from the background. **Interpolation across frames** was allowed, but every frame still had to be checked to ensure the box was centered correctly. **Brightness, contrast, and saturation** could be adjusted in CVAT to improve visibility. If a target was **partially occluded** or **indistinguishable from the background**, annotators were told to use the **best guess** and flag the box as **occluded**. Annotation of a target was to **start** when more than approximately **30%** of its pixels were visible in the scene, and **terminate** when more than approximately **70%** of its pixels were outside the scene. If a target exited and later re-entered, it received a **new ID**.

A notable property of MONET is its thermal-specific notion of visibility. In this dataset, “occlusion” includes not only geometric occlusion by another object but also cases where a target becomes thermally difficult to distinguish from the background or from nearby warm objects. The paper retains such boxes because they are informative for tracking and hard-case analysis [2304.05417].

## 4. Benchmark tasks and evaluation protocol

The paper benchmarks MONET primarily on **object detection / localisation**, using the **COCO evaluation procedure** and reporting **AP**, **AP\(_{50}\)**, and **AP\(_{75}\)**, together with class-wise AP. It evaluates **nine object detection algorithms**:

1. **Faster R-CNN**
2. **SSD**
3. **CornerNet**
4. **FCOS**
5. **DETR**
6. **Deformable DETR**
7. **VarifocalNet**
8. **ObjectBox**
9. **YOLOv8**

Most detectors were implemented through **MMDetection**, while **ObjectBox** and **YOLOv8** used the authors’ original implementations. The paper also reports **ObjectBox\(^\dagger\)** and **YOLOv8\(^\dagger\)**, which use the methods’ original augmentation policies [2304.05417].

To keep training conditions comparable, the paper states that all models were trained, as far as possible, with the **same data augmentations, backbones, and optimization parameters**. The common augmentation sequence was:

1. **RandomCrop** with crop factors in \([0.8,1.0]\)
2. **Resize** between \((600,800)\) and \((300,400)\), preserving aspect ratio
3. **RandomHorizontalFlip** with probability **0.7**
4. **Padding** to \((600,800)\)
5. **Normalization** with mean **126.225** and std **73.338**

The supplementary material states that **non-maximum suppression** used **IoU threshold 0.5**, and that all detector outputs with confidence above **\(10^{-8}\)** were evaluated.

MONET is also used as a benchmark for **transfer learning** and **domain adaptation**. The paper studies training on one MONET site and testing on the other, as well as transfer between MONET and **HIT-UAV**. This protocol is central to the paper’s argument that thermal modality alone does not guarantee generalization across rural sites or across aerial thermal datasets.

## 5. Difficulty profile and empirical findings

The empirical results show that MONET is challenging even for modern detectors. A central finding is that **runway is easier than dirt-road**. Same-site AP values are consistently higher on runway, while dirt-road is harder because **daytime ground heat** makes people blend into the background. The paper repeatedly notes that **person detection is generally harder than vehicle detection**, especially in dirt-road scenes [2304.05417].

The focused **Faster R-CNN** transfer study illustrates this. When trained on **dirt-road + runway**, the paper reports:

- **dirt-road V**: AP **24.9**
- **dirt-road T**: AP **36.3**
- **runway V**: AP **44.5**
- **runway T**: AP **42.1**

When training with **COCO init + pretrain on HIT-UAV + fine-tune on MONET**, the reported AP becomes **39.1** on **dirt-road T** and **46.2** on **runway T**, indicating that thermal pretraining on HIT-UAV can help before MONET fine-tuning. However, direct cross-dataset transfer is poor: training only on HIT-UAV yields AP **8.9** on **dirt-road T** and **12.7** on **runway T**, while training on MONET and testing on HIT-UAV yields AP **6.7** on **HIT-UAV V** and **7.1** on **HIT-UAV T**. Cross-site transfer within MONET is also weak: training on **dirt-road** and testing on **runway** gives **18.0** AP on **runway T**, and training on **runway** and testing on **dirt-road** gives **21.3** AP on **dirt-road T**.

The multi-detector comparison reinforces these conclusions. In the **same-scenario dirt-road \(\rightarrow\) dirt-road** setting, the best reported result is **YOLOv8\(^\dagger\)** with AP **33.3**, followed by **ObjectBox\(^\dagger\)** with AP **31.4**. In **same-scenario runway \(\rightarrow\) runway**, the best reported result is **YOLOv8** with AP **53.5**. In cross-site settings, performance drops substantially. The paper identifies **SSD** and **Deformable DETR** as particularly strong in transfer settings, while **YOLOv8** often has the strongest **AP\(_{75}\)**, which the authors interpret as suggesting better box-size localization.

Qualitative analysis adds a thermal-specific explanation for these failures. In dirt-road scenes, a person can become nearly indistinguishable from the ground because of similar heat signatures. The paper also notes that some false positives during transfer from HIT-UAV to MONET runway may arise because HIT-UAV contains many **parked vehicles** with relatively **low temperature**, causing models to associate dark thermal patterns with vehicles. This suggests that cross-dataset transfer failure is not merely a capacity problem; it is also a problem of thermal semantics and scene structure.

## 6. Distinguishing characteristics, uses, and naming ambiguity

MONET differs from previous thermal drone datasets because it combines **rural scenes captured with thermal cameras containing both person and vehicle targets**, **trajectory information**, and **timestamp-aligned metadata**. Its defining contribution is not only dataset scale but the combination of **moving multirotor viewpoints**, **two structurally different rural sites**, **identity-linked bounding boxes**, and **multimodal telemetry**. This makes it useful for research on **metadata-aware detection**, **motion-aware tracking**, **scale estimation from altitude**, and multimodal fusion between image content and UAV state [2304.05417].

A common misconception arises from the overloading of the name “MONET” in arXiv literature. The same label has been used for unrelated systems and models, including an Android malware variants detection system [1612.03312], an unsupervised scene decomposition model [1901.11390], and an open text-to-image corpus [2605.21272]. In the present sense, however, **MONET Dataset** refers specifically to the **multimodal drone thermal dataset recorded in rural scenarios** introduced in 2023 [2304.05417].

The dataset is publicly associated with the project page:

**https://github.com/fabiopoiesi/monet_dataset**

Its broader significance lies in making a particular failure mode of aerial perception measurable: even with the same thermal sensor and broadly similar rural geography, detectors trained on one site generalize poorly to another. This suggests that MONET functions not only as a benchmark for thermal object detection, but also as a controlled testbed for **cross-site robustness**, **multimodal aerial perception**, and **thermal-domain transfer**.

Source: https://www.emergentmind.com/topics/monet-dataset