車道線檢測LaneNet
- 2020 年 3 月 6 日
- 筆記
LaneNet
- LanNet
- Segmentation branch 完成語義分割,即判斷出像素屬於車道or背景
- Embedding branch 完成像素的向量表示,用於後續聚類,以完成實例分割
- H-Net
Segmentation branch
解決樣本分布不均衡
車道線像素遠小於背景像素.loss函數的設計對不同像素賦給不同權重,降低背景權重.
該分支的輸出為(w,h,2)．
Embedding branch
loss的設計思路為使得屬於同一條車道線的像素距離盡量小,屬於不同車道線的像素距離儘可能大.即Discriminative loss.
該分支的輸出為(w,h,n)．n為表示像素的向量的維度.
實例分割
在Segmentation branch完成語義分割,Embedding branch完成像素的向量表示後,做聚類,完成實例分割.
H-net
透視變換
to do
車道線擬合
LaneNet的輸出是每條車道線的像素集合，還需要根據這些像素點回歸出一條車道線。傳統的做法是將圖片投影到鳥瞰圖中，然後使用二次或三次多項式進行擬合。在這種方法中，轉換矩陣H只被計算一次，所有的圖片使用的是相同的轉換矩陣，這會導致坡度變化下的誤差。
為了解決這個問題，論文訓練了一個可以預測變換矩陣H的神經網路HNet，網路的輸入是圖片，輸出是轉置矩陣H。之前移植過Opencv逆透視變換矩陣的源碼，裡面轉換矩陣需要8個參數，這兒只給了6個參數的自由度，一開始有些疑惑，後來仔細閱讀paper，發現作者已經給出了解釋，是為了對轉換矩陣在水平方向上的變換進行約束。
程式碼分析
binary_seg_image, instance_seg_image = sess.run( [binary_seg_ret, instance_seg_ret], feed_dict={input_tensor: [image]} )輸入(１,256,512,3) 輸出binary_seg_image:(1, 256, 512) instance_seg_image:(1, 256, 512, 4)
完成像素級別的分類和向量表示
class LaneNet的inference分為兩步．
第一步提取分割的特徵,包括了用於語義分割的特徵和用以實例分割的特徵.
class LaneNet(cnn_basenet.CNNBaseModel): def inference(self, input_tensor, name): """ :param input_tensor: :param name: :return: """ with tf.variable_scope(name_or_scope=name, reuse=self._reuse): # first extract image features extract_feats_result = self._frontend.build_model( input_tensor=input_tensor, name='{:s}_frontend'.format(self._net_flag), reuse=self._reuse ) #得到一個字典,包含了用於語義分割的feature map和用於實例分割的feature map． #binary_segment_logits (1,256,512,2) 2是類別數目.即車道/背景． #instance_segment_logits (1,256,512,64) 用以後面再做卷積為每個像素生成一個向量表示 print('features:',extract_feats_result) # second apply backend process binary_seg_prediction, instance_seg_prediction = self._backend.inference( binary_seg_logits=extract_feats_result['binary_segment_logits']['data'], instance_seg_logits=extract_feats_result['instance_segment_logits']['data'], name='{:s}_backend'.format(self._net_flag), reuse=self._reuse ) if not self._reuse: self._reuse = True return binary_seg_prediction, instance_seg_prediction第一步得到的features如下:
features : OrderedDict([('encode_stage_1_share', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_encode_module/conv1_2/relu:0' shape=(1, 256, 512, 64) dtype=float32>, 'shape': [1, 256, 512, 64]}), ('encode_stage_2_share', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_encode_module/conv2_2/relu:0' shape=(1, 128, 256, 128) dtype=float32>, 'shape': [1, 128, 256, 128]}), ('encode_stage_3_share', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_encode_module/conv3_3/relu:0' shape=(1, 64, 128, 256) dtype=float32>, 'shape': [1, 64, 128, 256]}), ('encode_stage_4_share', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_encode_module/conv4_3/relu:0' shape=(1, 32, 64, 512) dtype=float32>, 'shape': [1, 32, 64, 512]}), ('encode_stage_5_binary', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_encode_module/conv5_3_binary/relu:0' shape=(1, 16, 32, 512) dtype=float32>, 'shape': [1, 16, 32, 512]}), ('encode_stage_5_instance', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_encode_module/conv5_3_instance/relu:0' shape=(1, 16, 32, 512) dtype=float32>, 'shape': [1, 16, 32, 512]}), ('binary_segment_logits', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_decode_module/binary_seg_decode/binary_final_logits/binary_final_logits:0' shape=(1, 256, 512, 2) dtype=float32>, 'shape': [1, 256, 512, 2]}), ('instance_segment_logits', {'data': <tf.Tensor 'lanenet_model/vgg_frontend/vgg16_decode_module/instance_seg_decode/decode_stage_1_fuse/fuse_feats:0' shape=(1, 256, 512, 64) dtype=float32>, 'shape': [1, 256, 512, 64]})]) 特徵提取完畢,做後處理
class LaneNetBackEnd(cnn_basenet.CNNBaseModel): def inference(self, binary_seg_logits, instance_seg_logits, name, reuse): """ :param binary_seg_logits: :param instance_seg_logits: :param name: :param reuse: :return: """ with tf.variable_scope(name_or_scope=name, reuse=reuse): with tf.variable_scope(name_or_scope='binary_seg'): binary_seg_score = tf.nn.softmax(logits=binary_seg_logits) binary_seg_prediction = tf.argmax(binary_seg_score, axis=-1) with tf.variable_scope(name_or_scope='instance_seg'): pix_bn = self.layerbn( inputdata=instance_seg_logits, is_training=self._is_training, name='pix_bn') pix_relu = self.relu(inputdata=pix_bn, name='pix_relu') instance_seg_prediction = self.conv2d( inputdata=pix_relu, out_channel=CFG.TRAIN.EMBEDDING_FEATS_DIMS, kernel_size=1, use_bias=False, name='pix_embedding_conv' ) return binary_seg_prediction, instance_seg_prediction對每個像素的分類,做softmax轉成概率.再argmax求概率較大值的下標. 對每個像素的向量表示,用1×1卷積核做卷積,得到channel維度=CFG.TRAIN.EMBEDDING_FEATS_DIMS(配置為4).即(1,256,512,64)卷積得到(1,256,512,4)的tensor．即每個像素用一個四維向量表示.
所以,整個LaneNet的inference返回的是兩個tensor.一個shape為(1,256,512) 一個為(1,256,512,4).
後處理
class LaneNetPostProcessor(object): def postprocess(self, binary_seg_result, instance_seg_result=None, min_area_threshold=100, source_image=None, data_source='tusimple'):對binary_seg_result,先通過形態學操作將小的空洞去除.參考https://www.cnblogs.com/sdu20112013/p/11672634.html
然後做聚類.
def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret): """ get lane embedding features according the binary seg result :param binary_seg_ret: :param instance_seg_ret: :return: """ idx = np.where(binary_seg_ret == 255) #idx (b,h,w) lane_embedding_feats = instance_seg_ret[idx] # idx_scale = np.vstack((idx[0] / 256.0, idx[1] / 512.0)).transpose() # lane_embedding_feats = np.hstack((lane_embedding_feats, idx_scale)) lane_coordinate = np.vstack((idx[1], idx[0])).transpose() assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0] ret = { 'lane_embedding_feats': lane_embedding_feats, 'lane_coordinates': lane_coordinate } return ret獲取到坐標及對應坐標像素對應的向量表示.
np.where(condition)
只有條件 (condition)，沒有x和y，則輸出滿足條件 (即非0) 元素的坐標 (等價於numpy.nonzero)。這裡的坐標以tuple的形式給出，通常原數組有多少維，輸出的tuple中就包含幾個數組，分別對應符合條件元素的各維坐標。
測試結果
tensorflow-gpu 1.15.2
4張titan xp
(4, 256, 512) (4, 256, 512, 4)
I0302 17:04:31.276140 29376 test_lanenet.py:222] imgae inference cost time: 2.58794s
(32, 256, 512) (32, 256, 512, 4)
I0302 17:05:50.322593 29632 test_lanenet.py:222] imgae inference cost time: 4.31036s
類似於高吞吐量,高延遲.對單幀圖片處理在1-2s,多幅圖片同時處理,平均下來的處理速度在0.1s.
論文里的backbone為enet,在nvida 1080 ti上推理速度52fps.
對於這個問題的解釋,作者的解釋是
2.Origin paper use Enet as backbone net but I use vgg16 as backbone net so speed will not get as fast as that. 3.Gpu need a short time to warm up and you can adjust your batch size to test the speed again:)
一個是特徵提取網路和論文里不一致,一個是gpu有一個短暫的warm up的時間.
我自己的測試結果是在extract image features耗時較多.換一個backbone可能會有改善.
def inference(self, input_tensor, name): """ :param input_tensor: :param name: :return: """ print("***************,input_tensor shape:",input_tensor.shape) with tf.variable_scope(name_or_scope=name, reuse=self._reuse): t_start = time.time() # first extract image features extract_feats_result = self._frontend.build_model( input_tensor=input_tensor, name='{:s}_frontend'.format(self._net_flag), reuse=self._reuse ) t_cost = time.time() - t_start glog.info('extract image features cost time: {:.5f}s'.format(t_cost)) # second apply backend process t_start = time.time() binary_seg_prediction, instance_seg_prediction = self._backend.inference( binary_seg_logits=extract_feats_result['binary_segment_logits']['data'], instance_seg_logits=extract_feats_result['instance_segment_logits']['data'], name='{:s}_backend'.format(self._net_flag), reuse=self._reuse ) t_cost = time.time() - t_start glog.info('backend process cost time: {:.5f}s'.format(t_cost)) if not self._reuse: self._reuse = True return binary_seg_prediction, instance_seg_prediction 參考:https://www.cnblogs.com/xuanyuyt/p/11523192.html https://zhuanlan.zhihu.com/p/93572094
