diff --git a/ai_controller/src-ncnn/aiengineinferencencnn.cpp b/ai_controller/src-ncnn/aiengineinferencencnn.cpp
index edd303a..b113fe9 100644
--- a/ai_controller/src-ncnn/aiengineinferencencnn.cpp
+++ b/ai_controller/src-ncnn/aiengineinferencencnn.cpp
@@ -1,6 +1,5 @@
 #include <QDebug>
 #include <QThread>
-#include <iostream>
 #include <vector>
 #include "aiengineinferencencnn.h"
 
@@ -23,7 +22,7 @@ char* getCharPointerCopy(const QString& modelPath) {
 AiEngineInferencevNcnn::AiEngineInferencevNcnn(QString modelPath, QObject *parent) :
     AiEngineInference{modelPath, parent}
 {
-    qDebug() << "TUOMAS AiEngineInferencevNcnn() mModelPath=" << mModelPath;
+    qDebug() << "AiEngineInferencevNcnn() mModelPath=" << mModelPath;
 
     yolov8.opt.num_threads        = 4;
     yolov8.opt.use_vulkan_compute = false;
@@ -32,9 +31,6 @@ AiEngineInferencevNcnn::AiEngineInferencevNcnn(QString modelPath, QObject *paren
     char *model = getCharPointerCopy(modelPath);
     char *param = getCharPointerCopy(paramPath);
 
-    qDebug() << "model:" << model;
-    qDebug() << "param:" << param;
-
     yolov8.load_param(param);
     yolov8.load_model(model);
 }
@@ -229,8 +225,6 @@ int AiEngineInferencevNcnn::detect_yolov8(const cv::Mat& bgr, std::vector<Object
     const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
     in_pad.substract_mean_normalize(0, norm_vals);
 
-    auto start = std::chrono::high_resolution_clock::now();
-
     ncnn::Extractor ex = yolov8.create_extractor();
 
     ex.input("in0", in_pad);
@@ -283,9 +277,6 @@ int AiEngineInferencevNcnn::detect_yolov8(const cv::Mat& bgr, std::vector<Object
         objects[i].rect.height = y1 - y0;
     }
 
-    auto end = std::chrono::high_resolution_clock::now();
-    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
-    std::cout << "Time taken: " << duration.count() << " milliseconds" << std::endl;
     return 0;
 }
 
@@ -340,8 +331,7 @@ static cv::Mat draw_objects(const cv::Mat& bgr, const std::vector<Object>& objec
 
         cv::Scalar cc(color[0], color[1], color[2]);
 
-        fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
-                obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
+        //fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
 
         cv::rectangle(image, obj.rect, cc, 2);
 
@@ -378,26 +368,23 @@ void AiEngineInferencevNcnn::performInferenceSlot(cv::Mat frame)
     std::vector<Object> objects;
     detect_yolov8(scaledImage, objects);
 
-    if (objects.empty() == false) {
-        AiEngineInferenceResult result;
-        result.frame = draw_objects(scaledImage, objects);
+    AiEngineInferenceResult result;
+    result.frame = draw_objects(scaledImage, objects);
 
-        for (uint i = 0; i < objects.size(); i++) {
-            const Object &detection = objects[i];
-            AiEngineObject object;
-            object.classId          = detection.label;
-            object.classStr         = mClassNames[detection.label];
-            object.propability      = detection.prob;
-            object.rectangle.top    = detection.rect.y;
-            object.rectangle.left   = detection.rect.x;
-            object.rectangle.bottom = detection.rect.y + detection.rect.height;
-            object.rectangle.right  = detection.rect.x + detection.rect.width;
-            result.objects.append(object);
-        }
-
-        emit resultsReady(result);
+    for (uint i = 0; i < objects.size(); i++) {
+        const Object &detection = objects[i];
+        AiEngineObject object;
+        object.classId          = detection.label;
+        object.classStr         = mClassNames[detection.label];
+        object.propability      = detection.prob;
+        object.rectangle.top    = detection.rect.y;
+        object.rectangle.left   = detection.rect.x;
+        object.rectangle.bottom = detection.rect.y + detection.rect.height;
+        object.rectangle.right  = detection.rect.x + detection.rect.width;
+        result.objects.append(object);
     }
 
+    emit resultsReady(result);
     mActive = false;
 }