请问为什么训练路侧相机检测车道线的模型大小有2G这么大？

[zqs1996]

[cfzd]

我已经在UFLDv2的仓库中回答了这个问题。
cfzd/Ultra-Fast-Lane-Detection-v2#56

[zqs1996]

我已经在UFLDv2的仓库中回答了这个问题。 cfzd/Ultra-Fast-Lane-Detection-v2#56

您好，大佬，请问我训练的模型应用在路侧相机端去检测车道线时，弯道车道线的检测效果是这样子（如贴图所示）？被拉长到图像底部了。谢谢

[cfzd]

@zqs1996
在数据处理时，我们默认会把车道线做延伸至图像底部的处理，你可以注释掉它，在这个部分：

Ultra-Fast-Lane-Detection/data/dataset.py

Lines 135 to 164 in 85eda71

 for i in range(self.num_lanes): 
     if np.all(all_idx_cp[i,:,1] == -1): 
         continue 
     # if there is no lane 
  
     valid = all_idx_cp[i,:,1] != -1 
     # get all valid lane points' index 
     valid_idx = all_idx_cp[i,valid,:] 
     # get all valid lane points 
     if valid_idx[-1,0] == all_idx_cp[0,-1,0]: 
         # if the last valid lane point's y-coordinate is already the last y-coordinate of all rows 
         # this means this lane has reached the bottom boundary of the image 
         # so we skip 
         continue 
     if len(valid_idx) < 6: 
         continue 
     # if the lane is too short to extend 
  
     valid_idx_half = valid_idx[len(valid_idx) // 2:,:] 
     p = np.polyfit(valid_idx_half[:,0], valid_idx_half[:,1],deg = 1) 
     start_line = valid_idx_half[-1,0] 
     pos = find_start_pos(all_idx_cp[i,:,0],start_line) + 1 
      
     fitted = np.polyval(p,all_idx_cp[i,pos:,0]) 
     fitted = np.array([-1  if y < 0 or y > w-1 else y for y in fitted]) 
  
     assert np.all(all_idx_cp[i,pos:,1] == -1) 
     all_idx_cp[i,pos:,1] = fitted 
 if -1 in all_idx[:, :, 0]: 
     pdb.set_trace()