diff --git a/motrackers/detectors/yolo.py b/motrackers/detectors/yolo.py
index ee169ec..dedd475 100644
--- a/motrackers/detectors/yolo.py
+++ b/motrackers/detectors/yolo.py
@@ -2,12 +2,9 @@
 import cv2 as cv
 from motrackers.detectors.detector import Detector
 from motrackers.utils.misc import load_labelsjson
-
-
 class YOLOv3(Detector):
     """
     YOLOv3 Object Detector Module.
-
     Args:
         weights_path (str): path to network weights file.
         configfile_path (str): path to network configuration file.
@@ -17,41 +14,32 @@ class YOLOv3(Detector):
         draw_bboxes (bool): If True, assign colors for drawing bounding boxes on the image.
         use_gpu (bool): If True, try to load the model on GPU.
     """
-
     def __init__(self, weights_path, configfile_path, labels_path, confidence_threshold=0.5, nms_threshold=0.2, draw_bboxes=True, use_gpu=False):
         self.net = cv.dnn.readNetFromDarknet(configfile_path, weights_path)
         object_names = load_labelsjson(labels_path)
-
         layer_names = self.net.getLayerNames()
         if cv.__version__ >= '4.6.0':
             self.layer_names = [layer_names[i - 1] for i in self.net.getUnconnectedOutLayers()]
         else:
-            self.layer_names = [layer_names[i[0] - 1] for i in self.net.getUnconnectedOutLayers()]
-
+            self.layer_names = [layer_names[i - 1] for i in self.net.getUnconnectedOutLayers()]
+            
         self.scale_factor = 1/255.0
         self.image_size = (416, 416)
-
         self.net = cv.dnn.readNetFromDarknet(configfile_path, weights_path)
         if use_gpu:
             self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
             self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)
-
         super().__init__(object_names, confidence_threshold, nms_threshold, draw_bboxes)
-
     def forward(self, image):
         blob = cv.dnn.blobFromImage(image, self.scale_factor, self.image_size, swapRB=True, crop=False)
         self.net.setInput(blob)
         detections = self.net.forward(self.layer_names)  # detect objects using object detection model
         return detections
-
     def detect(self, image):
         if self.width is None or self.height is None:
             (self.height, self.width) = image.shape[:2]
-
         detections = self.forward(image)
-
         bboxes, confidences, class_ids = [], [], []
-
         for output in detections:
             for detect in output:
                 scores = detect[5:]
@@ -63,8 +51,9 @@ def detect(self, image):
                     bboxes.append([x, y, w, h])
                     confidences.append(float(confidence))
                     class_ids.append(class_id)
-
+        if(bboxes == []):
+            return [], [], []
         indices = cv.dnn.NMSBoxes(bboxes, confidences, self.confidence_threshold, self.nms_threshold).flatten()
         class_ids = np.array(class_ids).astype('int')
         output = np.array(bboxes)[indices, :].astype('int'), np.array(confidences)[indices], class_ids[indices]
-        return output
+        return output
\ No newline at end of file