Add detection quality matrix

backend/README.md

@@ -389,6 +389,26 @@ with detection count, score, precision, recall, F1, mean IoU and false
 positive/negative counts. It is intended to tune confidence/IoU/model choices,
 not to add new inference behavior.
 
+To compare local model assets and tile settings as well as thresholds, run the
+quality matrix wrapper:
+
+```bash
+REAL_RASTER_PATH=/mnt/user/appdata/geointel/storage/operator-data/geel_orthophoto_wms_512.tif \
+REAL_REFERENCE_VECTOR_PATH=/mnt/user/appdata/geointel/storage/operator-data/geel_grb_gbg_buildings.geojson \
+QUALITY_MODEL_ASSET_IDS="yolov8n-building-segmentation-pt yolov8n-pt" \
+QUALITY_TILE_SIZES="512 640" \
+QUALITY_TILE_OVERLAPS="64" \
+QUALITY_THRESHOLDS="0.50 0.15" \
+bash scripts/run_detection_quality_matrix.sh http://192.168.10.150:1202
+```
+
+The matrix creates one real persisted workflow run per combination and writes
+`quality_matrix_summary.json` with the selected model asset, tile size, tile
+overlap, threshold, detection count, QA score, precision, recall, F1, mean IoU
+and false-positive/false-negative counts. It ranks `best_by_score`,
+`best_by_recall` and `best_by_precision`. It does not download weights, create
+fake detections, fetch live providers or change backend API behavior.
+
 To inspect the evidence behind a calibration run, export the persisted QA
 evidence bundle: