PhotonVision · Ruthie-FRC · Mar 15, 2026 · Mar 15, 2026 · Mar 15, 2026 · Mar 15, 2026
@@ -156,3 +156,4 @@ photon-client/playwright-report/
 photon-client/blob-report/
 photon-client/playwright/.cache/
 photon-client/playwright/.auth/
+photon-client/package-lock.json
@@ -95,7 +95,7 @@ const tabGroups = computed<ConfigOption[][]>(() => {
       tabGroup.filter(
         (tabConfig) =>
           !(!allow3d && tabConfig.tabName === "3D") && //Filter out 3D tab any time 3D isn't calibrated
-          !((!allow3d || isAprilTag || isAruco || isObjectDetection) && tabConfig.tabName === "PnP") && //Filter out the PnP config tab if 3D isn't available, or we're doing AprilTags
+          !((!allow3d || isAprilTag || isAruco) && tabConfig.tabName === "PnP") && //Filter out the PnP config tab if 3D isn't available, or we're doing AprilTags
           !((isAprilTag || isAruco || isObjectDetection) && tabConfig.tabName === "Threshold") && //Filter out threshold tab if we're doing AprilTags
           !((isAprilTag || isAruco || isObjectDetection) && tabConfig.tabName === "Contours") && //Filter out contours if we're doing AprilTags
           !(!isAprilTag && tabConfig.tabName === "AprilTag") && //Filter out apriltag unless we actually are doing AprilTags

@@ -48,7 +48,6 @@ const processingMode = computed<number>({
             :disabled="
               !useCameraSettingsStore().hasConnected ||
               !useCameraSettingsStore().isCurrentVideoFormatCalibrated ||
-              useCameraSettingsStore().currentPipelineSettings.pipelineType == PipelineType.ObjectDetection ||
               useCameraSettingsStore().currentPipelineSettings.pipelineType == PipelineType.ColoredShape
             "
             :variant="theme.global.name.value === 'LightTheme' ? 'elevated' : 'outlined'"

@@ -1,7 +1,7 @@
 <script setup lang="ts">
 import PvSelect from "@/components/common/pv-select.vue";
 import { useCameraSettingsStore } from "@/stores/settings/CameraSettingsStore";
-import { TargetModel } from "@/types/PipelineTypes";
+import { PipelineType, TargetModel } from "@/types/PipelineTypes";
 import PvSlider from "@/components/common/pv-slider.vue";
 import { computed } from "vue";
 import { useStateStore } from "@/stores/StateStore";
@@ -11,6 +11,10 @@ const { mdAndDown } = useDisplay();
 const interactiveCols = computed(() =>
   mdAndDown.value && (!useStateStore().sidebarFolded || useCameraSettingsStore().isDriverMode) ? 9 : 8
 );
+
+const isObjectDetection = computed(
+  () => useCameraSettingsStore().currentPipelineSettings.pipelineType === PipelineType.ObjectDetection
+);
 </script>
 
 <template>
@@ -34,6 +38,7 @@ const interactiveCols = computed(() =>
       "
     />
     <pv-slider
+      v-if="!isObjectDetection"
       v-model="useCameraSettingsStore().currentPipelineSettings.cornerDetectionAccuracyPercentage"
       class="pt-2"
       :slider-cols="interactiveCols"

@@ -30,9 +30,9 @@ export enum TargetModel {
   InfiniteRechargeHighGoalOuter = 2,
   CircularPowerCell7in = 3,
   RapidReactCircularCargoBall = 4,
-  AprilTag6in_16h5 = 5,
-  AprilTag6p5in_36h11 = 6,
-  ReefscapeAlgae = 7
+  ReefscapeAlgae = 5,
+  AprilTag6in_16h5 = 6,
+  AprilTag6p5in_36h11 = 7
 }
 
 export interface PipelineSettings {
@@ -313,8 +313,8 @@ export const DefaultObjectDetectionPipelineSettings: ObjectDetectionPipelineSett
   ...DefaultPipelineSettings,
   pipelineType: PipelineType.ObjectDetection,
   cameraGain: 20,
-  targetModel: TargetModel.InfiniteRechargeHighGoalOuter,
-  ledMode: true,
+  targetModel: TargetModel.ReefscapeAlgae,
+  ledMode: false,
   outputMaximumTargets: 20,
   cameraExposureRaw: 6,
   confidence: 0.9,

@@ -19,6 +19,7 @@
 
 import java.awt.Color;
 import java.lang.ref.Cleaner;
+import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.opencv.core.Mat;
@@ -33,8 +34,11 @@
 public class RknnObjectDetector implements ObjectDetector {
     private static final Logger logger = new Logger(RknnObjectDetector.class, LogGroup.General);
 
-    /** Cleaner instance to release the detector when it goes out of scope */
-    private final Cleaner cleaner = Cleaner.create();
+    /**
+     * Shared Cleaner instance for all RknnObjectDetector instances. Using a single static Cleaner
+     * avoids spawning a new daemon thread per detector object.
+     */
+    private static final Cleaner cleaner = Cleaner.create();
 
     /** Atomic boolean to ensure that the native object can only be released once. */
     private AtomicBoolean released = new AtomicBoolean(false);
@@ -128,10 +132,11 @@ public List<NeuralNetworkPipeResult> detect(Mat in, double nmsThresh, double box
             return List.of();
         }
 
-        return scale.resizeDetections(
-                List.of(results).stream()
-                        .map(it -> new NeuralNetworkPipeResult(it.rect, it.class_id, it.conf))
-                        .toList());
+        var pipeResults = new ArrayList<NeuralNetworkPipeResult>(results.length);
+        for (var it : results) {
+            pipeResults.add(new NeuralNetworkPipeResult(it.rect, it.class_id, it.conf));
+        }
+        return scale.resizeDetections(pipeResults);
     }
 
     /** Thread-safe method to release the detector. */

@@ -19,6 +19,7 @@
 
 import java.awt.Color;
 import java.lang.ref.Cleaner;
+import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.opencv.core.Mat;
@@ -33,8 +34,11 @@
 public class RubikObjectDetector implements ObjectDetector {
     private static final Logger logger = new Logger(RubikObjectDetector.class, LogGroup.General);
 
-    /** Cleaner instance to release the detector when it goes out of scope */
-    private final Cleaner cleaner = Cleaner.create();
+    /**
+     * Shared Cleaner instance for all RubikObjectDetector instances. Using a single static Cleaner
+     * avoids spawning a new daemon thread per detector object.
+     */
+    private static final Cleaner cleaner = Cleaner.create();
 
     /** Atomic boolean to ensure that the native object can only be released once. */
     private AtomicBoolean released = new AtomicBoolean(false);
@@ -113,10 +117,8 @@ public List<String> getClasses() {
     @Override
     public List<NeuralNetworkPipeResult> detect(Mat in, double nmsThresh, double boxThresh) {
         if (!isValid()) {
-            logger.error(
-                    "Detector is not initialized, and so it can't be released! Model: "
-                            + model.modelFile.getName());
-            return null;
+            logger.error("Detector is not initialized! Model: " + model.modelFile.getName());
+            return List.of();
         }
 
         // Resize the frame to the input size of the model
@@ -137,10 +139,11 @@ public List<NeuralNetworkPipeResult> detect(Mat in, double nmsThresh, double box
             return List.of();
         }
 
-        return scale.resizeDetections(
-                List.of(results).stream()
-                        .map(it -> new NeuralNetworkPipeResult(it.rect, it.class_id, it.conf))
-                        .toList());
+        var pipeResults = new ArrayList<NeuralNetworkPipeResult>(results.length);
+        for (var it : results) {
+            pipeResults.add(new NeuralNetworkPipeResult(it.rect, it.class_id, it.conf));
+        }
+        return scale.resizeDetections(pipeResults);
     }
 
     /** Thread-safe method to release the detector. */

@@ -52,7 +52,7 @@ protected List<NeuralNetworkPipeResult> process(CVMat in) {
             return List.of();
         }
 
-        return detector.detect(in.getMat(), params.nms(), params.confidence());
+        return detector.detect(frame, params.nms(), params.confidence());
     }
 
     public static record ObjectDetectionPipeParams(double confidence, double nms, Model model) {}

@@ -17,8 +17,10 @@
 
 package org.photonvision.vision.pipeline;
 
+import java.util.Arrays;
 import java.util.List;
 import java.util.Optional;
+import org.opencv.core.Point;
 import org.photonvision.common.configuration.NeuralNetworkModelManager;
 import org.photonvision.vision.frame.Frame;
 import org.photonvision.vision.frame.FrameThresholdType;
@@ -40,6 +42,7 @@ public class ObjectDetectionPipeline
     private final SortContoursPipe sortContoursPipe = new SortContoursPipe();
     private final Collect2dTargetsPipe collect2dTargetsPipe = new Collect2dTargetsPipe();
     private final FilterObjectDetectionsPipe filterContoursPipe = new FilterObjectDetectionsPipe();
+    private final SolvePNPPipe solvePNPPipe = new SolvePNPPipe();
 
     private static final FrameThresholdType PROCESSING_TYPE = FrameThresholdType.NONE;
 
@@ -99,6 +102,10 @@ protected void setPipeParamsImpl() {
                         settings.contourTargetOffsetPointEdge,
                         settings.contourTargetOrientation,
                         frameStaticProperties));
+
+        solvePNPPipe.setParams(
+                new SolvePNPPipe.SolvePNPPipeParams(
+                        frameStaticProperties.cameraCalibration, settings.targetModel));
     }
 
     @Override
@@ -125,11 +132,48 @@ protected CVPipelineResult process(Frame frame, ObjectDetectionPipelineSettings
                 collect2dTargetsPipe.run(sortContoursResult.output);
         sumPipeNanosElapsed += collect2dTargetsResult.nanosElapsed;
 
+        List<TrackedTarget> targetList;
+
+        // 3d stuff
+        if (settings.solvePNPEnabled) {
+            var rectPoints = new Point[4];
+            collect2dTargetsResult.output.forEach(
+                    target -> {
+                        target.getMinAreaRect().points(rectPoints);
+                        if (settings.targetModel.isSpherical()) {
+                            // For symmetric (spherical) targets such as balls: the model presents
+                            // the same circular silhouette from every angle, so any mapping of the
+                            // 4 OBB corners to the 4 equatorial model points yields the same pose.
+                            // Just hand the corners through as-is.
+                            target.setTargetCorners(
+                                    Arrays.asList(rectPoints[0], rectPoints[1], rectPoints[2], rectPoints[3]));
+                        } else {
+                            // For non-symmetric targets the OBB side ratio indicates which face of
+                            // the 3D object is visible.  RotatedRect.points() returns corners in
+                            // order (bottom-left, top-left, top-right, bottom-right); reorder to
+                            // (bottom-left, bottom-right, top-right, top-left) to match the
+                            // solvePNP model convention expected by CornerDetectionPipe.
+                            // TODO: when TargetModel gains multi-face support for 3D non-symmetric
+                            // objects, select the appropriate face's 3D corners based on the ratio
+                            // of the OBB's width to its height before calling solvePNP.
+                            target.setTargetCorners(
+                                    Arrays.asList(rectPoints[0], rectPoints[3], rectPoints[2], rectPoints[1]));
+                        }
+                    });
+
+            var solvePNPResult = solvePNPPipe.run(collect2dTargetsResult.output);
+            sumPipeNanosElapsed += solvePNPResult.nanosElapsed;
+
+            targetList = solvePNPResult.output;
+        } else {
+            targetList = collect2dTargetsResult.output;
+        }
+
         var fpsResult = calculateFPSPipe.run(null);
         var fps = fpsResult.output;
 
         return new CVPipelineResult(
-                frame.sequenceID, sumPipeNanosElapsed, fps, collect2dTargetsResult.output, frame, names);
+                frame.sequenceID, sumPipeNanosElapsed, fps, targetList, frame, names);
     }
 
     @Override

@@ -17,10 +17,13 @@
 
 package org.photonvision.vision.pipeline;
 
+import com.fasterxml.jackson.annotation.JsonTypeName;
 import org.photonvision.common.configuration.NeuralNetworkModelManager;
 import org.photonvision.common.configuration.NeuralNetworkModelsSettings;
 import org.photonvision.vision.objects.Model;
+import org.photonvision.vision.target.TargetModel;
 
+@JsonTypeName("ObjectDetectionPipelineSettings")
 public class ObjectDetectionPipelineSettings extends AdvancedPipelineSettings {
     public double confidence;
     public double nms; // non maximal suppression
@@ -33,6 +36,9 @@ public ObjectDetectionPipelineSettings() {
         cameraExposureRaw = 20;
         cameraAutoExposure = false;
         ledMode = false;
+        // Use a spherical ball model by default: YOLO primarily detects game pieces like
+        // balls/cargo, which are symmetric from all sides and work well with solvePNP.
+        targetModel = TargetModel.k2025Algae;
         confidence = .9;
         nms = .45;
         model =

@@ -217,6 +217,13 @@ public MatOfPoint3f getVisualizationBoxTop() {
         return visualizationBoxTop;
     }
 
+    public boolean isSpherical() {
+        return switch (this) {
+            case kCircularPowerCell7in, k2022CircularCargoBall, k2025Algae -> true;
+            default -> false;
+        };
+    }
+
     //    public static TargetModel getCircleTarget(double Units.inchesToMeters(7)) {
     //        var corners =
     //            List.of(