---
license: cc-by-4.0
library_name: keras
pipeline_tag: object-detection
tags:
- person-detection
- search-and-rescue
- drone
- aerial
- tinyml
- tflite
- int8
- esp32
- centernet
---

# voxOCR: tiny person detector for drone search and rescue

A CenterNet-style, anchor-free person detector for downward-looking drone
cameras: **136,547 parameters, 197 KB full-int8 TFLite**, built for
ESP32-S3-class microcontrollers. It marks where people are in a 160×160 RGB
tile so a drone can flag places worth a closer look for a rescue team.

> **Türkçe özet:** Arama kurtarma dronları için, mikrodenetleyicide
> çalışacak kadar küçük (136 bin parametre, int8 dosyası 197 KB) bir insan
> tespit modeli. HERIDAL test setinde int8 hâli, eşik 0,2'de insanların
> %82,5'ini buluyor. CC BY 4.0.

Code and training details: [ErdemWilkinson/voxOCR](https://github.com/ErdemWilkinson/voxOCR).

> **Newer version on GitHub (2026-11-01):** v3, trained 7 more hours from
> these weights, finds 83.9% of people at threshold 0.2 as int8 QAT, with
> 0.35 false alarms per tile (the files here, v2: 82.5% and 0.47). Scores:
> [voxOCR v3](https://github.com/ErdemWilkinson/voxOCR#v3-2026-11-01-7-more-hours-from-v2).

## Files

| File | What it is |
|---|---|
| `voxocr_qat_int8.tflite` | **Recommended.** Quantization-aware trained, full int8, input `[1,160,160,3]` int8, output `[1,40,40,3]` int8 |
| `voxocr_int8.tflite` | Post-training quantized int8 of the float model |
| `voxocr.keras` | Float model |
| `evaluation.json` | Recall / precision / false alarms per threshold, val and test |

## Input and output

- **Input:** 160×160 RGB, `pixel / 127.5 - 1`. A 640×640 crop of a
  4000×3000 aerial photo, resized to 160, matches the test setting.
- **Output:** 40×40 grid (stride 4).
  - Channel 0: person-centre heatmap (0 to 1).
  - Channels 1 and 2: `log` box width and height in grid cells.
- **Detections:** local maxima of channel 0 above a threshold. Lower
  thresholds find more people and give more false alarms.

```python
import numpy as np
import tensorflow as tf
from PIL import Image

interpreter = tf.lite.Interpreter(model_path="voxocr_qat_int8.tflite")
interpreter.allocate_tensors()
inp, out = interpreter.get_input_details()[0], interpreter.get_output_details()[0]
(si, zi), (so, zo) = inp["quantization"], out["quantization"]

tile = np.asarray(Image.open("tile.png").convert("RGB").resize((160, 160)), np.float32) / 127.5 - 1
interpreter.set_tensor(inp["index"], np.clip(np.round(tile / si + zi), -128, 127).astype(np.int8)[None])
interpreter.invoke()
grid = (interpreter.get_tensor(out["index"])[0].astype(np.float32) - zo) * so

heat = grid[..., 0]
peaks = heat == tf.nn.max_pool2d(heat[None, :, :, None], 3, 1, "SAME")[0, :, :, 0].numpy()
for y, x in zip(*np.nonzero(peaks & (heat >= 0.2))):
    w, h = np.exp(grid[y, x, 1:]) * 4
    print(f"person at ({(x + 0.5) * 4:.0f}, {(y + 0.5) * 4:.0f}) px, about {w:.0f}x{h:.0f}, score {heat[y, x]:.2f}")
```

## Results (HERIDAL test set, 3,507 tiles, 372 people)

A detection is a find when its centre is inside a person's box grown by one
grid cell.

![Recall and precision on the HERIDAL test set, v1 and v2](voxocr-test-v1-v2-light.svg)

| Model, threshold | Recall | Precision | False alarms per tile |
|---|---:|---:|---:|
| int8 QAT, 0.45 (best F1) | 59.7% | 52.5% | 0.06 |
| int8 QAT, 0.30 | 74.5% | 29.8% | 0.19 |
| **int8 QAT, 0.20** | **82.5%** | 15.8% | 0.47 |
| int8 QAT, 0.10 | 86.6% | 5.5% | 1.58 |
| float, 0.45 (best F1) | 57.0% | 55.1% | 0.05 |

The int8 file loses nothing measurable against the float model. v1 (an
under-trained first run, recall 28.8% at best F1) is shown in the chart for
comparison.

## Limitations

- Trained and tested only on HERIDAL: summer, daytime, mostly Mediterranean
  terrain. Snow, forest canopy, water, night and thermal are untested.
- 90% recall is not reached on the test set.
- Not yet measured on an ESP32-S3 (speed, memory) or on real flight video.

## Data and license

Trained on [HERIDAL](https://zenodo.org/records/5662351) (CC BY 3.0):
D. Božić-Štulić, Ž. Marušić, S. Gotovac, "Deep Learning Approach in Aerial
Imagery for Supporting Land Search and Rescue Missions", IJCV 2019.

Weights: **CC BY 4.0**. Training code: MIT.

## Intended use

Search and rescue: helping find people who are lost or injured. It is not
intended for targeting or harming people.