diff --git a/config.py b/config.py
index cfc6424..ff0b31e 100644
--- a/config.py
+++ b/config.py
@@ -26,6 +26,9 @@ class Config:
     # blk_model_path = r"/home/dt/tobacco-color/weights/rf_4x4_c22_20_sen8_9.model"  # 机器上部署的路径
     spec_size_threshold = 3
 
+    s_threshold_a = 125  # s_a的最高允许值
+    s_threshold_b = 125  # s_b的最高允许值
+
     # rgb模型参数
     rgb_tobacco_model_path = r"weights/tobacco_dt_2022-08-27_14-43.model"  # 开发时的路径
     # rgb_tobacco_model_path = r"/home/dt/tobacco-color/weights/tobacco_dt_2022-08-27_14-43.model"  # 机器上部署的路径
diff --git a/models/__init__.py b/models/__init__.py
index b52b2e5..06c125f 100755
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -337,7 +337,7 @@ class RgbDetector(Detector):
         lab_b = cv2.cvtColor(rgb_data, cv2.COLOR_RGB2LAB)[..., 2] < Config.threshold_b
         lab_predict_result = lab_a | lab_b
         mask_lab = size_threshold(lab_predict_result, Config.blk_size, Config.lab_size_threshold)
-        mask_rgb = mask_rgb | mask_lab
+        mask_rgb = mask_rgb.astype(np.uint8) | mask_lab.astype(np.uint8)
         # # 测试时间
         # end = time.time()
         # print("lab time: ", end - start)
@@ -370,9 +370,19 @@ class SpecDetector(Detector):
         pixel_predict_result = self.pixel_predict_ml_dilation(data=img_data, iteration=1)
         blk_predict_result = self.blk_predict(data=img_data)
         mask = (pixel_predict_result & blk_predict_result).astype(np.uint8)
+        spec_cv = np.clip(img_data[..., [21, 3, 0]], a_min=0, a_max=1) * 255
+        spec_cv = spec_cv.astype(np.uint8)
+        # spec转lab 提取a通道，识别绿色杂质
+        lab_a = cv2.cvtColor(spec_cv, cv2.COLOR_RGB2LAB)[..., 1] < Config.s_threshold_a
+        # spec转lab 提取b通道，识别蓝色杂质
+        lab_b = cv2.cvtColor(spec_cv, cv2.COLOR_RGB2LAB)[..., 2] < Config.s_threshold_b
+        lab_predict_result = lab_a | lab_b
+        mask_lab = size_threshold(lab_predict_result, Config.blk_size, Config.lab_size_threshold)
+
         if save_part:
             self.spare_part = mask[-(Config.blk_size//2):, :]
         mask = size_threshold(mask, Config.blk_size, Config.spec_size_threshold, self.spare_part)
+        mask = mask.astype(np.uint8) | mask_lab.astype(np.uint8)
         return mask
 
     def save(self, *args, **kwargs):