Suppress duplicate YOLO tile detections

backend/app/core/config.py

@@ -32,6 +32,7 @@ class Settings(BaseSettings):
     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_duplicate_iou_threshold: float = Field(default=0.5, ge=0.0, le=1.0, validation_alias="YOLO_DUPLICATE_IOU_THRESHOLD")
     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/detection_service.py

@@ -138,7 +138,7 @@ class DetectionService:
 
         if model.model_id == resolved_settings.yolo_model_id:
             try:
-                detections = DetectionService._run_configured_yolo(
+                detections, postprocess_summary = DetectionService._run_configured_yolo(
                     db=db,
                     project_id=project_id,
                     dataset_id=dataset_id,
@@ -165,7 +165,7 @@ class DetectionService:
                     error_code=exc.code,
                     message=exc.message,
                 )
-            DetectionService._mark_success(db, analysis_run, job, detection_count=len(detections))
+            DetectionService._mark_success(db, analysis_run, job, detection_count=len(detections), extra_result=postprocess_summary)
             return DetectionRunResponse(
                 analysis_run_id=analysis_run.id,
                 job_id=job.id,
@@ -454,8 +454,10 @@ class DetectionService:
         db.refresh(job)
 
     @staticmethod
-    def _mark_success(db, analysis_run: AnalysisRun, job: Job, detection_count: int) -> None:
+    def _mark_success(db, analysis_run: AnalysisRun, job: Job, detection_count: int, extra_result: dict[str, Any] | None = None) -> None:
         result = {"detection_count": detection_count}
+        if extra_result:
+            result.update(extra_result)
         analysis_run.status = "success"
         analysis_run.finished_at = DetectionService._now()
         analysis_run.result_json = result
@@ -536,13 +538,13 @@ class DetectionService:
         class_filter: list[str],
         settings: Settings,
         yolo_adapter_class: Type[YoloDetectionAdapter],
-    ) -> list[Detection]:
+    ) -> tuple[list[Detection], dict[str, Any]]:
         manifest = DetectionService._load_tile_manifest(tile_manifest_path, settings.yolo_max_tiles)
         model_path = Path(settings.yolo_model_path or "").expanduser()
         adapter = yolo_adapter_class(settings)
         model = adapter.load_model(model_path)
         allowed_classes = {DetectionService._canonical_class_name(value) for value in class_filter if DetectionService._canonical_class_name(value)}
-        persisted: list[Detection] = []
+        candidates: list[dict[str, Any]] = []
         manifest_crs = manifest.get("crs") or manifest.get("source_crs") or manifest.get("dataset_crs") or "EPSG:4326"
         for tile in manifest["tiles"]:
             tile_path = DetectionService._resolve_tile_path(tile, Path(tile_manifest_path or "").expanduser())
@@ -561,37 +563,87 @@ class DetectionService:
                 properties = dict(raw.get("properties") or {})
                 if model_class_name and model_class_name != class_name:
                     properties.setdefault("model_class_name", model_class_name)
-                detection = Detection(
-                    id=uuid.uuid4(),
-                    project_id=project_id,
-                    dataset_id=dataset_id,
-                    analysis_run_id=analysis_run.id,
-                    job_id=job.id,
-                    model_name=model_name,
-                    model_version=model_version,
-                    class_name=class_name,
-                    confidence=confidence,
-                    geometry=from_shape(geometry, srid=4326),
-                    bbox_json={
-                        "x_min": float(bbox[0]),
-                        "y_min": float(bbox[1]),
-                        "x_max": float(bbox[2]),
-                        "y_max": float(bbox[3]),
-                    },
-                    source_tile_path=str(tile_path),
-                    properties_json={**properties, "tile_index": tile.get("index")},
+                candidates.append(
+                    {
+                        "class_name": class_name,
+                        "confidence": confidence,
+                        "geometry": geometry,
+                        "bbox": bbox,
+                        "source_tile_path": str(tile_path),
+                        "properties": {**properties, "tile_index": tile.get("index")},
+                    }
                 )
-                db.add(detection)
-                persisted.append(detection)
+        filtered_candidates = DetectionService._suppress_duplicate_candidates(
+            candidates,
+            iou_threshold=float(settings.yolo_duplicate_iou_threshold),
+        )
+        persisted: list[Detection] = []
+        for candidate in filtered_candidates:
+            bbox = candidate["bbox"]
+            detection = Detection(
+                id=uuid.uuid4(),
+                project_id=project_id,
+                dataset_id=dataset_id,
+                analysis_run_id=analysis_run.id,
+                job_id=job.id,
+                model_name=model_name,
+                model_version=model_version,
+                class_name=candidate["class_name"],
+                confidence=candidate["confidence"],
+                geometry=from_shape(candidate["geometry"], srid=4326),
+                bbox_json={
+                    "x_min": float(bbox[0]),
+                    "y_min": float(bbox[1]),
+                    "x_max": float(bbox[2]),
+                    "y_max": float(bbox[3]),
+                },
+                source_tile_path=candidate["source_tile_path"],
+                properties_json=candidate["properties"],
+            )
+            db.add(detection)
+            persisted.append(detection)
         db.commit()
         for detection in persisted:
             db.refresh(detection)
-        return persisted
+        return persisted, {
+            "raw_detection_count": len(candidates),
+            "suppressed_detection_count": len(candidates) - len(filtered_candidates),
+            "duplicate_iou_threshold": float(settings.yolo_duplicate_iou_threshold),
+        }
 
     @staticmethod
     def _canonical_class_name(value: Any) -> str:
         return str(value or "").strip().casefold()
 
+    @staticmethod
+    def _suppress_duplicate_candidates(candidates: list[dict[str, Any]], iou_threshold: float) -> list[dict[str, Any]]:
+        if iou_threshold <= 0 or len(candidates) < 2:
+            return candidates
+        kept: list[dict[str, Any]] = []
+        for candidate in sorted(candidates, key=lambda item: float(item["confidence"]), reverse=True):
+            duplicate = False
+            for kept_candidate in kept:
+                if candidate["class_name"] != kept_candidate["class_name"]:
+                    continue
+                if DetectionService._geometry_iou(candidate["geometry"], kept_candidate["geometry"]) >= iou_threshold:
+                    duplicate = True
+                    break
+            if not duplicate:
+                kept.append(candidate)
+        return kept
+
+    @staticmethod
+    def _geometry_iou(left, right) -> float:
+        if left.is_empty or right.is_empty:
+            return 0.0
+        intersection_area = left.intersection(right).area
+        if intersection_area <= 0:
+            return 0.0
+        union_area = left.union(right).area
+        if union_area <= 0:
+            return 0.0
+        return intersection_area / union_area
+
     @staticmethod
     def _load_tile_manifest(tile_manifest_path: str | None, max_tiles: int) -> dict[str, Any]:
         if not tile_manifest_path: