Raise configured YOLO max detections

backend/README.md

@@ -359,6 +359,7 @@ YOLO_CONFIG_DIR=/app/storage/ultralytics
 YOLO_DEVICE=cpu
 YOLO_IMAGE_SIZE=640
 YOLO_MAX_TILES=100
+YOLO_MAX_DETECTIONS=1000
 YOLO_BATCH_SIZE=1
 ```
 
@@ -384,6 +385,10 @@ python scripts/yolo_preflight.py --model-path /absolute/path/to/local-model.pt -
 
 The preflight checks configuration, dependency availability, local model file existence, tile manifest validity, tile count and referenced tile paths. JSON output also includes runtime diagnostics for the model directory, `YOLO_CONFIG_DIR`, installed `torch`/`ultralytics` versions and CUDA availability when dependency checks pass. It does not load a YOLO model, run inference or download weights.
 
+`YOLO_MAX_DETECTIONS` is forwarded to Ultralytics as `max_det`. The default is
+`1000` because dense building AOIs can exceed the upstream default cap of 300
+detections before QA/QC can measure recall honestly.
+
 The same read-only status is available through the API and Detection Lab UI:
 
 ```bash

backend/app/core/config.py

@@ -31,6 +31,7 @@ class Settings(BaseSettings):
     yolo_device: str = Field(default="cpu", validation_alias="YOLO_DEVICE")
     yolo_image_size: int = Field(default=640, validation_alias="YOLO_IMAGE_SIZE")
     yolo_max_tiles: int = Field(default=100, validation_alias="YOLO_MAX_TILES")
+    yolo_max_detections: int = Field(default=1000, validation_alias="YOLO_MAX_DETECTIONS")
     yolo_batch_size: int = Field(default=1, validation_alias="YOLO_BATCH_SIZE")
     cors_origins: list[str] | str = Field(
         default=["http://localhost:5173", "http://127.0.0.1:5173"],

backend/app/services/yolo_adapter.py

@@ -72,6 +72,7 @@ class YoloDetectionAdapter:
                     conf=float(confidence_threshold),
                     imgsz=int(self.settings.yolo_image_size),
                     device=self.settings.yolo_device,
+                    max_det=int(self.settings.yolo_max_detections),
                     verbose=False,
                 )
         except AppError:

backend/tests/test_docker_runtime_config.py

@@ -93,6 +93,7 @@ def test_env_example_uses_runtime_env_names_read_by_backend_and_frontend() -> No
     assert "YOLO_MODEL_PATH=" in env_example
     assert "YOLO_CONFIG_DIR=./storage/ultralytics" in env_example
     assert "YOLO_MAX_TILES=100" in env_example
+    assert "YOLO_MAX_DETECTIONS=1000" in env_example
     assert "ENABLE_YOLO" not in env_example
     assert "ENABLE_SAM" not in env_example
     assert "VITE_API_BASE_URL=" in env_example
@@ -245,4 +246,5 @@ def test_unraid_deploy_passes_ai_build_arg_and_yolo_runtime_env() -> None:
     assert '-e YOLO_MODELS_DIR="$YOLO_MODELS_DIR"' in run_script
     assert '-e YOLO_MODEL_PATH="$YOLO_MODEL_PATH"' in run_script
     assert '-e YOLO_MAX_TILES="$YOLO_MAX_TILES"' in run_script
+    assert '-e YOLO_MAX_DETECTIONS="$YOLO_MAX_DETECTIONS"' in run_script
     assert "-v \"${GEOINTEL_MODELS_PATH}:/app/models\"" in run_script

backend/tests/test_sprint8b_yolo_foundation.py

@@ -93,7 +93,7 @@ class RecordingPredictModel:
     def __init__(self) -> None:
         self.seen_sources: list[dict] = []
 
-    def predict(self, *, source, conf, imgsz, device, verbose):
+    def predict(self, *, source, conf, imgsz, device, verbose, max_det):
         from PIL import Image
 
         with Image.open(source) as image:
@@ -106,6 +106,7 @@ class RecordingPredictModel:
                     "imgsz": imgsz,
                     "device": device,
                     "verbose": verbose,
+                    "max_det": max_det,
                 }
             )
         return []
@@ -399,6 +400,20 @@ def test_yolo_adapter_converts_single_band_tiles_to_rgb_before_prediction(tmp_pa
     assert model.seen_sources[0]["imgsz"] == 64
     assert model.seen_sources[0]["device"] == "cpu"
     assert model.seen_sources[0]["verbose"] is False
+    assert model.seen_sources[0]["max_det"] == 1000
+
+
+def test_yolo_adapter_uses_configured_max_detections(tmp_path: Path) -> None:
+    Image = pytest.importorskip("PIL.Image")
+    tile_path = tmp_path / "rgb_tile.png"
+    Image.new("RGB", (16, 16), (10, 20, 30)).save(tile_path)
+    model = RecordingPredictModel()
+    settings = _settings(tmp_path, yolo_max_detections=1500)
+
+    detections = YoloDetectionAdapter(settings).predict_tile(model, tile_path, confidence_threshold=0.25)
+
+    assert detections == []
+    assert model.seen_sources[0]["max_det"] == 1500
 
 
 def test_yolo_adapter_wraps_prediction_runtime_errors(tmp_path: Path) -> None: