diff --git a/src/main/java/org/jdetect/CameraApp.java b/src/main/java/org/jdetect/CameraApp.java
index ad54282..0985d92 100644
--- a/src/main/java/org/jdetect/CameraApp.java
+++ b/src/main/java/org/jdetect/CameraApp.java
@@ -663,82 +663,103 @@ class DetectionThread extends SwingWorker<Mat, Void> {
             return frame;
         }
 
-        Size frameSize = frame.size();
-        int height = (int) frameSize.height;
-        int width = (int) frameSize.width;
+        try {
+            Size frameSize = frame.size();
+            int height = (int) frameSize.height;
+            int width = (int) frameSize.width;
 
-        // Create blob from image
-        Mat blob = Dnn.blobFromImage(frame, 1.0/255.0, new Size(416, 416), new Scalar(0), true, false);
+            // Create blob from image
+            Mat blob = Dnn.blobFromImage(frame, 1.0/255.0, new Size(416, 416),
+                    new Scalar(0), true, false);
 
-        // Set input to the network
-        net.setInput(blob);
+            // Set input to the network
+            net.setInput(blob);
 
-        // Run forward pass
-        List<Mat> outs = new ArrayList<>();
-        net.forward(outs, outputLayers);
+            // Run forward pass
+            List<Mat> outs = new ArrayList<>();
+            net.forward(outs, outputLayers);
 
-        // Parse detections
-        List<Integer> classIds = new ArrayList<>();
-        List<Float> confidences = new ArrayList<>();
-        List<Rect2d> boxes = new ArrayList<>();
+            // Parse detections
+            List<Integer> classIds = new ArrayList<>();
+            List<Float> confidences = new ArrayList<>();
+            List<Rect2d> boxes = new ArrayList<>();
 
-        for (Mat out : outs) {
-            for (int i = 0; i < out.rows(); i++) {
-                Mat row = out.row(i);
-                Mat scores = row.colRange(5, row.cols());
-                Core.MinMaxLocResult mmr = Core.minMaxLoc(scores);
-                double confidence = mmr.maxVal;
+            for (Mat out : outs) {
+                // Convert Mat to float array
+                float[] data = new float[(int) out.total() * out.channels()];
+                out.get(0, 0, data);
 
-                if (confidence > 0.5) {
-                    Point classIdPoint = mmr.maxLoc;
-                    int classId = (int) classIdPoint.x;
+                // Process each detection (85 elements per detection: [x, y, w, h, confidence, class_probs...])
+                for (int i = 0; i < out.rows(); i++) {
+                    int offset = i * out.cols();
+                    float confidence = data[offset + 4];
 
-                    double[] detection = row.get(0, 0);
-                    double centerX = detection[0] * width;
-                    double centerY = detection[1] * height;
-                    double boxWidth = detection[2] * width;
-                    double boxHeight = detection[3] * height;
+                    if (confidence > 0.5) {
+                        // Find class with maximum score
+                        int classId = 0;
+                        float maxScore = 0;
+                        for (int j = 5; j < out.cols(); j++) {
+                            if (data[offset + j] > maxScore) {
+                                maxScore = data[offset + j];
+                                classId = j - 5;
+                            }
+                        }
 
-                    double x = centerX - (boxWidth / 2);
-                    double y = centerY - (boxHeight / 2);
+                        // Only add detection if classId is valid
+                        if (classId >= 0 && classId < classes.size()) {
+                            float centerX = data[offset] * width;
+                            float centerY = data[offset + 1] * height;
+                            float boxWidth = data[offset + 2] * width;
+                            float boxHeight = data[offset + 3] * height;
 
-                    boxes.add(new Rect2d(x, y, boxWidth, boxHeight));
-                    confidences.add((float) confidence);
-                    classIds.add(classId);
+                            float x = centerX - (boxWidth / 2);
+                            float y = centerY - (boxHeight / 2);
+
+                            boxes.add(new Rect2d(x, y, boxWidth, boxHeight));
+                            confidences.add(confidence);
+                            classIds.add(classId);
+                        }
+                    }
                 }
             }
-        }
 
-        // Apply Non-Maximum Suppression
-        MatOfRect2d boxesMat = new MatOfRect2d();
-        boxesMat.fromList(boxes);
-        MatOfFloat confidencesMat = new MatOfFloat();
-        confidencesMat.fromList(confidences);
-        MatOfInt indices = new MatOfInt();
+            // Apply Non-Maximum Suppression
+            if (!boxes.isEmpty()) {
+                MatOfRect2d boxesMat = new MatOfRect2d();
+                boxesMat.fromList(boxes);
+                MatOfFloat confidencesMat = new MatOfFloat();
+                confidencesMat.fromList(confidences);
+                MatOfInt indices = new MatOfInt();
 
-        Dnn.NMSBoxes(boxesMat, confidencesMat, 0.5f, 0.4f, indices);
+                Dnn.NMSBoxes(boxesMat, confidencesMat, 0.5f, 0.4f, indices);
 
-        // Draw detections
-        int[] indicesArray = indices.toArray();
-        for (int i : indicesArray) {
-            if (i < boxes.size() && i < classIds.size() && i < confidences.size()) {
-                Rect2d box = boxes.get(i);
-                String label = classes.get(classIds.get(i));
-                float confidence = confidences.get(i);
+                // Draw detections
+                int[] indicesArray = indices.toArray();
+                for (int i : indicesArray) {
+                    if (i >= 0 && i < boxes.size() && i < classIds.size() && i < confidences.size()) {
+                        Rect2d box = boxes.get(i);
+                        String label = classes.get(classIds.get(i));
+                        float confidence = confidences.get(i);
 
-                Scalar color = new Scalar(0, 255, 0);
-                Point topLeft = new Point(box.x, box.y);
-                Point bottomRight = new Point(box.x + box.width, box.y + box.height);
+                        Scalar color = new Scalar(0, 255, 0);
+                        Point topLeft = new Point(box.x, box.y);
+                        Point bottomRight = new Point(box.x + box.width, box.y + box.height);
 
-                Imgproc.rectangle(frame, topLeft, bottomRight, color, 2);
+                        Imgproc.rectangle(frame, topLeft, bottomRight, color, 2);
 
-                String text = String.format("%s: %.2f", label, confidence);
-                Point textPoint = new Point(box.x, box.y - 5);
-                Imgproc.putText(frame, text, textPoint, Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, color, 2);
+                        String text = String.format("%s: %.2f", label, confidence);
+                        Point textPoint = new Point(box.x, box.y - 5);
+                        Imgproc.putText(frame, text, textPoint,
+                                Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, color, 2);
+                    }
+                }
             }
-        }
 
-        return frame;
+            return frame;
+        } catch (Exception e) {
+            e.printStackTrace();
+            return frame; // Return original frame if detection fails
+        }
     }
 
     @Override