diff --git a/src/models/det_model/inference.py b/src/models/det_model/inference.py
index a142d16..5e0dd2c 100644
--- a/src/models/det_model/inference.py
+++ b/src/models/det_model/inference.py
@@ -1,4 +1,5 @@
 import os
+import time
 import yaml
 import numpy as np
 import cv2
@@ -90,6 +91,9 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
     subimg_save_dir = os.path.join(imgsave_dir, 'subimages')
     os.makedirs(subimg_save_dir, exist_ok=True)
 
+    first_image_skipped = False
+    total_time = 0
+    num_images = 0
     # predict image
     for img_path in tqdm(img_list):
         img = cv2.imread(img_path)
@@ -102,8 +106,21 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
         inputs_name = [var.name for var in predictor.get_inputs()]
         inputs = {k: inputs[k][None, ] for k in inputs_name}
 
+        # Start timing
+        start_time = time.time()
+
         outputs = predictor.run(output_names=None, input_feed=inputs)
 
+        # Stop timing
+        end_time = time.time()
+        inference_time = end_time - start_time
+        if not first_image_skipped:
+            first_image_skipped = True
+        else:
+            total_time += inference_time
+            num_images += 1
+        print(f"ONNXRuntime predict time for {os.path.basename(img_path)}: {inference_time:.4f} seconds")
+
         print("ONNXRuntime predict: ")
         if infer_config.arch in ["HRNet"]:
             print(np.array(outputs[0]))
@@ -130,12 +147,29 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
                 subimg_counter += 1
 
         # Draw bounding boxes and save the image with bounding boxes
+        img_with_mask = img.copy()
+        for output in np.array(outputs[0]):
+            cls_id, score, xmin, ymin, xmax, ymax = output
+            if score > infer_config.draw_threshold:
+                cv2.rectangle(img_with_mask, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (255, 255, 255), -1) # 盖白
+        
         img_with_bbox = draw_bbox(img, np.array(outputs[0]), infer_config)
+
         output_dir = imgsave_dir
         os.makedirs(output_dir, exist_ok=True)
-        output_file = os.path.join(output_dir, "output_" + os.path.basename(img_path))
-        cv2.imwrite(output_file, img_with_bbox)
+        draw_box_dir = os.path.join(output_dir, 'draw_box')
+        mask_white_dir = os.path.join(output_dir, 'mask_white')
+        os.makedirs(draw_box_dir, exist_ok=True)
+        os.makedirs(mask_white_dir, exist_ok=True)
 
+        output_file_mask = os.path.join(mask_white_dir, os.path.basename(img_path))
+        output_file_bbox = os.path.join(draw_box_dir, os.path.basename(img_path))
+        cv2.imwrite(output_file_mask, img_with_mask)
+        cv2.imwrite(output_file_bbox, img_with_bbox)
+
+    avg_time_per_image = total_time / num_images if num_images > 0 else 0
+    print(f"Total inference time for {num_images} images: {total_time:.4f} seconds")
+    print(f"Average time per image: {avg_time_per_image:.4f} seconds")
     print("ErrorImgs:")
     print(errImgList)