Drone detection models

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LICENSE

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README.md

@@ -0,0 +1,149 @@
+---
+license: agpl-3.0
+language:
+- ru
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+- yolov8
+- ultralytics
+- object-detection
+- drone-detection
+- anti-uav
+- small-object-detection
+datasets:
+- GabrieleMagrini/FRED
+model-index:
+- name: Ястраб
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      name: DUT Anti-UAV (test)
+      type: dut-anti-uav
+    metrics:
+    - type: map_50
+      value: 0.928
+  - task:
+      type: object-detection
+    dataset:
+      name: Halmstad Drone Dataset (test)
+      type: halmstad-drone-dataset
+    metrics:
+    - type: map_50
+      value: 0.856
+  - task:
+      type: object-detection
+    dataset:
+      name: Anti-UAV300 RGB (test)
+      type: anti-uav300
+    metrics:
+    - type: map_50
+      value: 0.959
+---
+
+# Ястраб: детектор дронов с наземной камеры
+
+Это модели из моей исследовательской работы «Автономный перехватчик БПЛА». Камера смотрит в небо, модель находит на кадре квадрокоптер, трекер его ведёт, а пусковая установка наводится и стреляет водяным шариком. Код всего комплекса лежит на [GitHub](https://github.com/ReNothingg/track_drone), здесь только веса.
+
+Заметить дрон по картинке трудно: в кадре он часто занимает около 10 пикселей, фон сложный, а птицы и самолёты на него похожи. В прошлом году я обучал свою языковую модель Mind 4 и понял, что универсальную модель в одиночку не построить. Поэтому в этот раз я сделал модель под 1 задачу и проверил, может ли она обойти универсальные решения там, где важна точность. Вся работа сделана на ноутбуке с Apple M5 и арендованном на полтора часа сервере с H100. Итоговые затраты около 45 $.
+
+![Drone Studio: слева рамка детектора на ролике, справа кадр камеры ствола с прицелом](assets/studio_frames.png)
+
+## Какие модели здесь лежат
+
+Обучение шло поэтапно, и каждой версии я дал имя птицы.
+
+| Папка | Модель | Что это |
+| --- | --- | --- |
+| `production/` | Ястраб | основная: YOLOv8s, 11,1 млн параметров, вход 640 px, 13 мс на кадр на M5. Рядом лежат specialist для мелких целей и проверяющая сеть |
+| `yastrab/` | Ястраб | та же модель отдельно, с описанием обучения |
+| `berkut/` | Беркут | Ястраб, дообученный на входе 960 px. Лучше на DUT, хуже на Halmstad |
+| `studio/` | Ястраб-Studio | дообучен на 3 070 кадрах 3D-сцены Drone Studio, для симулятора |
+| `observation/` | профиль наблюдения | основная и дообученная модели плюс проверяющая сеть MobileNetV3. Добавлены FRED и сложные фоны Open Images, ложных тревог стало 11 вместо 90 |
+
+Рядом с каждым `.pt` лежит `.json`: контрольная сумма, откалиброванный порог уверенности и результаты на тестах. Порог Ястраба подобран на проверочной выборке по максимуму F1 и равен 0,335.
+
+## Как устроена и как обучалась
+
+Ястраб - свёрточная сеть из 3 частей. Основа сжимает кадр 640 × 640 в карты признаков. Шея соединяет карты 3 масштабов, чтобы мелкий дрон на 16 пикселей и крупный на 100 искались одинаково хорошо. Голова для каждой клетки сеток 80 × 80, 40 × 40 и 20 × 20 предсказывает, есть ли там дрон, и 4 расстояния до сторон рамки. Пересекающиеся рамки одной цели убирает NMS при IoU 0,55.
+
+Ястраба я учил на сервере с H100 1,5 часа: 40 эпох на 21 071 кадре из 3 наборов. Среди них 3 869 кадров, на которые я вклеил вырезанных дронов шириной 7-40 px, чтобы сеть насмотрелась на мелкие цели. Чтобы сеть не заучила кадры, каждый при обучении случайно меняется: склейка 4 кадров в 1, сдвиг цвета и яркости, поворот, масштаб, отражение.
+
+| Набор | Что внутри | Обучение | Проверка | Тест |
+| --- | --- | ---: | ---: | ---: |
+| [DUT Anti-UAV](https://github.com/wangdongdut/DUT-Anti-UAV) | фотографии дронов на разном фоне | 5 200 | 2 600 | 2 199 |
+| [Halmstad](https://github.com/DroneDetectionThesis/Drone-detection-dataset) | 285 роликов: дроны, птицы, самолёты, вертолёты | 6 295 | 1 346 | 1 340 |
+| [Anti-UAV300](https://github.com/ZhaoJ9014/Anti-UAV) | 318 роликов, день и ночь | 4 377 | 946 | 2 147 |
+| Дополнение | сложные фоны и мелкие дроны, только из train | 1 330 | - | - |
+
+Перед обучением я проверил данные. По контрольным суммам нашёлся кадр, который был одновременно и в обучающей, и в тестовой части DUT, и я убрал его из тестов, чтобы модель не полагалась на заученный ответ. В Anti-UAV300 26 съёмочных сессий попали и в обучение, и в проверку, поэтому сессии я разделил заново. Ещё 83 кадра Halmstad с видимым, но неразмеченным дроном я убрал, чтобы не учить сеть считать дрон фоном.
+
+![Ход обучения: mAP@0,5 на проверочной выборке по эпохам](assets/training.png)
+
+## Результаты
+
+Модели проверены на 3 тестах из кадров, которых они не видели при обучении. mAP@0,5 от 0 до 1, где 1 - все дроны найдены без ложных тревог. Время кадра замерено на M5.
+
+| Модель | DUT | Halmstad | Anti-UAV300 | Среднее | мс | Вывод |
+| --- | ---: | ---: | ---: | ---: | ---: | --- |
+| Бусел | 0,899 | 0,011 | 0,822 | 0,577 | 8 | слепа на Halmstad |
+| Сокал | 0,888 | 0,839 | 0,971 | 0,899 | 8 | прежняя основная |
+| Арол | 0,930 | 0,807 | 0,959 | 0,899 | 17 | ложные тревоги |
+| **Ястраб** | **0,928** | **0,856** | **0,959** | **0,914** | **13** | **основная, лучшая** |
+| Беркут | 0,944 | 0,783 | 0,952 | 0,893 | 25 | хуже на Halmstad |
+| drone-yolov8 | 0,242 | 0,012 | 0,476 | 0,243 | - | готовая чужая |
+| flying-objects | 0,148 | 0,007 | 0,071 | 0,075 | - | готовая чужая |
+
+![Средняя оценка моделей на 3 тестах](assets/models_ranking.png)
+
+Результат Бусла сам по себе удивил: на своём наборе он работает хорошо, а в роликах Halmstad почти не находит дроны, ведь съёмка, фон и размер целей там другие. Бусел учился 15 эпох, Сокал только 5, но на роликах нужного типа, и выиграл. Отсюда главный вывод: данные нужного типа съёмки важнее, чем долгое обучение на 1 наборе.
+
+Ради интереса я прогнал и варианты потяжелее: YOLOv8m и сеть с головой P2 на 1280 пикселях. На DUT они дали 0,946 и 0,963, а на Halmstad 0,808 и 0,774. Не помогла и добавка 48 тыс. чужих кадров из 23 открытых наборов: на Halmstad минус 5 пунктов. Вывод вышел сам собой: чем больше сеть, вход или чужих данных, тем лучше DUT и хуже Halmstad. Поэтому основным я оставил Ястраба. 2 готовые открытые модели с Hugging Face на тех же тестах мелкие дроны не видят вовсе.
+
+## Против универсальных нейросетей
+
+Я взял 30 кадров из отложенной выборки Halmstad, где дрон часто меньше 20 пикселей, и дал их моей модели и ChatGPT. Каждой сети кадры даны по одному, без инструментов, одним и тем же вопросом. Рамка засчитывается, если её перекрытие с разметкой (IoU) не меньше 0,5.
+
+| Модель | Найдено из 30 | Точных рамок | Средний IoU |
+| --- | ---: | ---: | ---: |
+| Ястраб, 640 px | 30 | 26 | 0,66 |
+| Арол, 960 px | 28 | 26 | 0,72 |
+| ChatGPT 6 Instant | 20 | 2 | 0,32 |
+
+ChatGPT пропустил все 10 дронов уже 20 пикселей, а там, где нашёл, рамка почти всегда шире нужной.
+
+![30 кадров Halmstad: белая рамка - разметка, зелёная - Ястраб (Y), жёлтая - ChatGPT (G), в подписи ширина дрона и IoU](assets/benchmark30_models.png)
+
+На 3 кадрах с дроном 76, 31 и 16 пикселей я сравнил ещё Gemini 5.3 Flash Lite и Grok. Когда дрон виден явно или слабо, универсальные сети его находят. А на кадре с дроном в 16 пикселей все 3 ставят рамку мимо или заметно меньше дрона. Ястраб и Арол находят все 3.
+
+И для интереса 2 мои модели за 2 года:
+
+|  | Mind 4, 2025 | Ястраб, 2026 |
+| --- | --- | --- |
+| Задача | универсальная языковая модель | 1 класс «дрон» на кадре |
+| Архитектура | Transformer, 36 слоёв, 128 экспертов | YOLOv8s, 11,1 млн параметров |
+| Данные | 650 млн токенов из интернета без фильтрации | 21 071 кадр из 3 наборов, вычищенных вручную |
+| Железо | Google Colab, A100 | ноутбук M5 и 1,5 ч H100, 45 $ |
+| Итог на тесте | TruthfulQA около 30 %, галлюцинации | mAP@0,5 = 0,914, 26 точных рамок из 30 |
+| Против лучших | хуже GPT-2 (2019) | лучше ChatGPT (2026) на своей задаче |
+
+## Как пользоваться
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+weights = hf_hub_download("ReNothingg/drone-studio", "production/best.pt")
+model = YOLO(weights)
+result = model.predict("frame.jpg", imgsz=640, conf=0.335)[0]
+print(result.boxes.xyxy, result.boxes.conf)
+```
+
+Чтобы скачать все модели сразу и запустить Drone Studio с трекером и 3D-сценой, склонируйте [репозиторий](https://github.com/ReNothingg/track_drone) и выполните `npm install` и `npm run dev`. Веса скачаются сами при первом запуске.
+
+## Ограничения
+
+Модель проверена на открытых наборах, а не на моих видео с птицами и другими дронами. На незнакомый источник сеть переносится плохо: обученная на DUT и Halmstad, на Anti-UAV300 она нашла лишь 47 % дронов, так что данные нужны под каждую камеру. Модель видит только 1 класс «дрон» и не отличает самолётные БПЛА. Цифры выше относятся к указанным тестам и не обещают такой же точности на другой камере.
+
+Установка из работы учебная: она стреляет водяными шариками и не предназначена для применения против людей или чужого имущества.

berkut/best.json

@@ -0,0 +1,63 @@
+{
+  "name": "Беркут",
+  "created_at": "2026-10-07",
+  "architecture": "yolov8s",
+  "image_size": 960,
+  "source": "runs/h100/drone_s960/best.pt (RunPod H100, 8 epochs at 960 px from runs/h100/drone_s640/best.pt)",
+  "sha256": "ab25be23058b457366675db001989aa94ff76ed9f9e2760b58defe32d954cd45",
+  "inference_confidence": 0.3363363363363363,
+  "sensitive_confidence": 0.2,
+  "confidence_calibration": {
+    "created_at": "2026-10-07T00:01:42.456051+00:00",
+    "split": "val",
+    "image_size": 960,
+    "criterion": "maximum_f1",
+    "threshold": 0.3363363363363363,
+    "precision": 0.9388784051358676,
+    "recall": 0.8992114342040414,
+    "f1": 0.9186169023475065
+  },
+  "server_eval_map50": {
+    "dut_yolo/val": {
+      "map50": 0.916,
+      "map": 0.6006,
+      "precision": 0.9454,
+      "recall": 0.8784
+    },
+    "dut_yolo/test": {
+      "map50": 0.9441,
+      "map": 0.6397,
+      "precision": 0.9482,
+      "recall": 0.9046
+    },
+    "halmstad_yolo_v2/val": {
+      "map50": 0.7813,
+      "map": 0.308,
+      "precision": 0.8002,
+      "recall": 0.8226
+    },
+    "halmstad_yolo_v2/test": {
+      "map50": 0.7869,
+      "map": 0.3235,
+      "precision": 0.7953,
+      "recall": 0.8349
+    },
+    "anti_uav300_rgb_v1/val": {
+      "map50": 0.9941,
+      "map": 0.6876,
+      "precision": 0.9976,
+      "recall": 0.9924
+    },
+    "anti_uav300_rgb_v1/test": {
+      "map50": 0.957,
+      "map": 0.538,
+      "precision": 0.947,
+      "recall": 0.9079
+    }
+  },
+  "studio_synth_val": {
+    "map50": 0.82,
+    "recall": 0.792
+  },
+  "latency_ms_m5_960": 24.5
+}

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observation/primary.json

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+  "sensitive_confidence": 0.22999999999999998,
+  "purpose": "independent observation"
+}

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+  "support_confidence": 0.2,
+  "recovery_confidence": 0.31,
+  "recovery_verifier_floor": 0.5,
+  "voting": 0.5,
+  "validated": true,
+  "purpose": "independent observation; no actuator interface",
+  "weights": "primary.pt",
+  "student_weights": "student.pt",
+  "verifier_weights": "verifier.ts",
+  "tracking_confidence": 0.31,
+  "sha256": "afd0d805f1caa7321c3e8bef29cce819f9cf4f9f42ac3196672ac14bc3ee5195",
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+    "primary.json": "17146a594452e4cf1207e2dfff3b4a9a983f018f9291a72de6fc57448c443bb1"
+  },
+  "selection_split": "val",
+  "validation_file": "runs/eval/observation_v7/consensus_val.json",
+  "test_file": "runs/eval/observation_v7/release_test.summary.json",
+  "test_gate_passed": false,
+  "promotion_eligible": false,
+  "validation_scope": "Val-selected observation profile; held-out test has small per-source recall regressions documented in the report",
+  "source_student": "runs/observation/train/observation_v7_stage2/weights/best.pt",
+  "source_verifier": "runs/observation/verifier_v7/verifier.ts"
+}

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