Suppress duplicate YOLO tile detections

backend/tests/test_sprint8b_yolo_foundation.py

@@ -89,6 +89,25 @@ class MixedCaseYoloAdapter(MockYoloAdapter):
         ]
 
 
+class OverlappingTileYoloAdapter(MockYoloAdapter):
+    def predict_tile(self, model, tile_path: Path, confidence_threshold: float) -> list[dict]:
+        tile_index = int(tile_path.stem.split("_")[-1])
+        if tile_index == 0:
+            bbox = [10.0, 20.0, 30.0, 40.0]
+            confidence = 0.82
+        else:
+            bbox = [11.0, 21.0, 31.0, 41.0]
+            confidence = 0.91
+        return [
+            {
+                "class_name": "building",
+                "confidence": confidence,
+                "bbox": bbox,
+                "properties": {"adapter": "overlap"},
+            }
+        ]
+
+
 class RecordingPredictModel:
     def __init__(self) -> None:
         self.seen_sources: list[dict] = []
@@ -383,6 +402,37 @@ def test_yolo_class_filter_is_case_insensitive_and_persists_canonical_class(tmp_
     assert detections[0].properties_json["model_class_name"] == "Building"
 
 
+def test_yolo_run_suppresses_cross_tile_duplicate_detections(tmp_path: Path) -> None:
+    db, project_id, dataset_id = _project_and_dataset()
+    model_path = tmp_path / "model.pt"
+    model_path.write_bytes(b"local weights")
+    settings = _settings(tmp_path, yolo_model_path=str(model_path), yolo_duplicate_iou_threshold=0.5)
+    manifest_path = _manifest(tmp_path, tile_count=2)
+
+    result = DetectionService.run_detection(
+        db=db,
+        project_id=project_id,
+        dataset_id=dataset_id,
+        model_id="yolo-configured",
+        confidence_threshold=0.5,
+        class_filter=["building"],
+        tile_manifest_path=str(manifest_path),
+        settings=settings,
+        yolo_adapter_class=OverlappingTileYoloAdapter,
+    )
+
+    detections = [item for item in db.added if isinstance(item, Detection)]
+    runs = [item for item in db.added if isinstance(item, AnalysisRun)]
+
+    assert result.status == "success"
+    assert result.detection_count == 1
+    assert detections[0].confidence == 0.91
+    assert detections[0].source_tile_path.endswith("tile_0001.tif")
+    assert runs[0].result_json["raw_detection_count"] == 2
+    assert runs[0].result_json["suppressed_detection_count"] == 1
+    assert runs[0].result_json["duplicate_iou_threshold"] == 0.5
+
+
 def test_yolo_adapter_converts_single_band_tiles_to_rgb_before_prediction(tmp_path: Path) -> None:
     Image = pytest.importorskip("PIL.Image")
     tile_path = tmp_path / "single_band_tile.tif"