diff --git a/utils/utils_bbox.py b/utils/utils_bbox.py
index d1b0954539a32db287e32fabc0ce217ad589e4b1..6c203dd285d0f91f936aea9962418df20fad81e4 100644
--- a/utils/utils_bbox.py
+++ b/utils/utils_bbox.py
@@ -120,33 +120,6 @@ class DecodeBox():
             outputs.append(output.data)
         return outputs
 
-    def yolo_correct_boxes(self, box_xy, box_wh, angle, input_shape, image_shape, letterbox_image):
-        #-----------------------------------------------------------------#
-        #   把y轴放前面是因为方便预测框和图像的宽高进行相乘
-        #-----------------------------------------------------------------#
-        box_yx = box_xy[..., ::-1]
-        box_hw = box_wh[..., ::-1]
-        input_shape = np.array(input_shape)
-        image_shape = np.array(image_shape)
-
-        if letterbox_image:
-            #-----------------------------------------------------------------#
-            #   这里求出来的offset是图像有效区域相对于图像左上角的偏移情况
-            #   new_shape指的是宽高缩放情况
-            #-----------------------------------------------------------------#
-            new_shape = np.round(image_shape * np.min(input_shape/image_shape))
-            offset  = (input_shape - new_shape)/2./input_shape
-            scale   = input_shape/new_shape
-
-            box_yx  = (box_yx - offset) * scale
-            box_hw *= scale
-
-        box_xy = box_yx[..., ::-1] * image_shape
-        box_wh = box_hw[..., ::-1] * image_shape
-
-        boxes  = np.concatenate([box_xy[..., 0:1], box_xy[..., 1:2], box_wh[..., 0:1], box_wh[..., 1:2], angle[..., 0:1] ], axis=-1)
-        return boxes
-
     def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
         #----------------------------------------------------------#
         #   prediction  [batch_size, num_anchors, 85]
@@ -205,28 +178,11 @@ class DecodeBox():
                 )
                 max_detections = detections_class[keep]
                 
-                # # 按照存在物体的置信度排序
-                # _, conf_sort_index = torch.sort(detections_class[:, 4]*detections_class[:, 5], descending=True)
-                # detections_class = detections_class[conf_sort_index]
-                # # 进行非极大抑制
-                # max_detections = []
-                # while detections_class.size(0):
-                #     # 取出这一类置信度最高的，一步一步往下判断，判断重合程度是否大于nms_thres，如果是则去除掉
-                #     max_detections.append(detections_class[0].unsqueeze(0))
-                #     if len(detections_class) == 1:
-                #         break
-                #     ious = bbox_iou(max_detections[-1], detections_class[1:])
-                #     detections_class = detections_class[1:][ious < nms_thres]
-                # # 堆叠
-                # max_detections = torch.cat(max_detections).data
-                
                 # Add max detections to outputs
                 output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
             
             if output[i] is not None:
-                output[i]             = output[i].cpu().numpy()
-                box_xy, box_wh, angle = output[i][:, 0:2], output[i][:, 2:4], output[i][:, 4:5]
-                output[i][:, :5]      = self.yolo_correct_boxes(box_xy, box_wh, angle, input_shape, image_shape, letterbox_image)
+                output[i] = output[i].cpu().numpy()
         return output
 
 if __name__ == "__main__":
diff --git a/yolo.py b/yolo.py
index e739683e97874d816fc226a6db232cf45a0581b4..ab053df1c05244b01498c1dad887b46152cf9f52 100644
--- a/yolo.py
+++ b/yolo.py
@@ -158,6 +158,11 @@ class YOLO(object):
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
             top_polys   = rbox2poly(top_rboxes)
+            #---------------------------------------------------------#
+            #   将归一化的预测结果变为真实的预测框
+            #---------------------------------------------------------#
+            top_polys[..., [0, 2, 4, 6]] *= image_shape[0]
+            top_polys[..., [1, 3, 5, 7]] *= image_shape[1]
         #---------------------------------------------------------#
         #   设置字体与边框厚度
         #---------------------------------------------------------#