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# <img width="154" height="78" alt="leasard_logo_LowRes" src="https://github.com/user-attachments/assets/167b1981-1a0d-48fb-86ae-f634b9d182aa" /> Lola YOLO - Low-Latency Object Detection for Search-and-Rescue Drones

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## 📄 [SSRR'25] Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones

### 🔍 Abstract
Drones play a crucial role in search and rescue missions by providing real-time information on areas of interest that are difficult to access or endangering to human rescuers. However, analyzing raw video feeds by human operators to detect objects of interest, such as vehicles or victims, becomes increasingly demanding as the mission duration increases. This underscores the need for embedded computer vision to reduce the operator's cognitive load and enhance mission responsiveness. Towards this goal, we propose a low-latency object detection model based on YOLO, fitted to SAR missions, and able to process data coming from RGB and event cameras. We also propose a low-latency implementation on FPGA on board drones, achieving accurate detection in less than 20ms. Through a series of tests performed indoors using a prototype drone, we highlight the features of the model and processing core that favor drone reactivity and operational autonomy.

### 📚 Paper
👉 [Read the Paper](https://your-paper-url.com)

[![Watch the video](https://img.youtube.com/vi/OiB0UMzM5M4/0.jpg)](https://www.youtube.com/watch?v=OiB0UMzM5M4)

---
## Acknowledgement

This work was supported by Labex CominLabs excellence
laboratory managed by the French National Research Agency in the
“Investing for the Future” program under reference ANR-10-LABX-0007-01, project LEASARD, and by the French National Research Agency project VORTEX 2023-28, under reference ANR-23-IAS2-0005.

## ⚙️ Installation Instructions

### 💻 Virtual Environment (Python 3.10 recommended)

```bash
python3 -m venv venv
source venv/bin/activate
pip install -r lola_requirements.txt
```

---

## 🏋️ Training

Make sure to replace:
- `<DATASET>` with your dataset YAML name
- `<MODE>` with either `4bits`, `8bits`, or `float`

```bash
python train.py --data data/<DATASET>.yaml \
                --cfg models/lola_yolo_<MODE>.yaml \
                --weights '' \
                --batch-size 16 \
                --hyp data/hyps/hyp.scratch.p5.yaml \
                --cos-lr \
                --epochs 50 \
                --name train_test
```

---

## ✅ Validation & 🔍 Detection

### 🔎 Run detection on custom data

```bash
python quant_detect.py \
    --weights runs/train/<NAME_OF_TRAINING>/weights/best.pt \
    --cfg models/<MODEL>.yaml \
    --source <SOURCE_PATH>
```

### ✅ Run validation

```bash
python quant_val.py \
    --weights runs/train/<NAME_OF_TRAINING>/weights/best.pt \
    --data data/<DATASET>.yaml \
    --img 640
```

---

## 👨‍💻 Authors

- [Ismail AMESSEGHER](https://github.com/iamessegher),Arthur Gaudard,Kojo Nyamekye Anyinam-Boateng,[Hugo Le Blevec](https://github.com/hleblevec), Lionel Génevé, Florian Pouthier, Mathieu Léonardon, Hajer Fradi, Lucia Bergantin, Panagiotis Papadakis, Isabelle Fantoni, Jean-Philippe Diguet, Matthieu Arzel

lola_yolo

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