diff --git a/ginka/model/loss.py b/ginka/model/loss.py
index 2a5ab09..f196d7b 100644
--- a/ginka/model/loss.py
+++ b/ginka/model/loss.py
@@ -47,14 +47,13 @@ def outer_border_constraint_loss(pred: torch.Tensor, allowed_classes=[1, 11], pe
     return penalty_scale * loss
 
 def _create_distance_kernel(size):
-    """生成带距离权重的卷积核"""
-    kernel = torch.zeros(2*size-1, 2*size-1)
-    center = size-1
-    for i in range(2*size-1):
-        for j in range(2*size-1):
-            dist = math.sqrt((i-center)**2 + (j-center)**2)
-            kernel[i,j] = 1 / (1 + dist)  # 距离越近权重越高
-    return kernel.view(1,1,2*size-1,2*size-1)
+    """生成一个环状衰减核"""
+    y, x = torch.meshgrid(torch.arange(size), torch.arange(size), indexing='ij')
+    center = size // 2
+    dist = torch.sqrt((x - center)**2 + (y - center)**2)
+    kernel = torch.exp(-dist / (size / 2))  # 高斯衰减
+    kernel /= kernel.sum()  # 归一化
+    return kernel.unsqueeze(0).unsqueeze(0)  # [1,1,H,W]
 
 def entrance_constraint_loss(
     pred: torch.Tensor,
@@ -102,9 +101,6 @@ def entrance_constraint_loss(
     
     # 平滑惩罚函数：S形曲线
     spacing_loss = torch.sigmoid(10*(density_map - 0.5)).mean()  # 密度>0.5时快速上升
-    
-    # print(entrance_probs)
-    print(presence_loss.item(), (density_map).mean().item(), center_weight.mean().item())
 
     ###########################
     # 区域加权综合损失
@@ -274,11 +270,6 @@ def entrance_spatial_constraint(
     ##########################################
     # 2. 边缘区域约束（只能出现箭头）
     ##########################################
-    # 提取边缘区域的箭头概率 [B, N_edge_pixels]
-    edge_arrow_probs = pred_probs[:, arrow_class][:, edge_mask]
-    
-    # 边缘应最大化箭头概率（最小化1 - arrow_prob）
-    edge_arrow_loss = (1 - edge_arrow_probs).mean()
     
     # 抑制边缘出现楼梯的概率 [B, N_edge_pixels]
     edge_stair_probs = pred_probs[:, stair_class][:, edge_mask]
@@ -287,11 +278,6 @@ def entrance_spatial_constraint(
     ##########################################
     # 3. 中间区域约束（只能出现楼梯）
     ##########################################
-    # 提取中间区域的楼梯概率 [B, N_center_pixels]
-    center_stair_probs = pred_probs[:, stair_class][:, center_mask]
-    
-    # 中间应最大化楼梯概率（最小化1 - stair_prob）
-    center_stair_loss = (1 - center_stair_probs).mean()
     
     # 抑制中间出现箭头的概率 [B, N_center_pixels]
     center_arrow_probs = pred_probs[:, arrow_class][:, center_mask]
@@ -301,8 +287,8 @@ def entrance_spatial_constraint(
     # 4. 综合损失
     ##########################################
     total_loss = (
-        lambda_arrow * (edge_arrow_loss + edge_stair_penalty) +
-        lambda_stair * (center_stair_loss + center_arrow_penalty)
+        lambda_arrow * edge_stair_penalty +
+        lambda_stair * center_arrow_penalty
     )
     
     return total_loss