车道线检测LaneNet

LaneNet

• LanNet　
  • Segmentation branch　完成语义分割,即判断出像素属于车道or背景
  • Embedding branch　完成像素的向量表示,用于后续聚类,以完成实例分割
• H-Net

Segmentation branch

解决样本分布不均衡　　　

车道线像素远小于背景像素.loss函数的设计对不同像素赋给不同权重,降低背景权重.

该分支的输出为(w,h,2)．

Embedding branch

loss的设计思路为使得属于同一条车道线的像素距离尽量小,属于不同车道线的像素距离尽可能大.即Discriminative loss.

该分支的输出为(w,h,n)．n为表示像素的向量的维度.

实例分割

在Segmentation branch完成语义分割,Embedding branch完成像素的向量表示后,做聚类,完成实例分割.

H-net

透视变换

to do

车道线拟合

LaneNet的输出是每条车道线的像素集合，还需要根据这些像素点回归出一条车道线。传统的做法是将图片投影到鸟瞰图中，然后使用二次或三次多项式进行拟合。在这种方法中，转换矩阵H只被计算一次，所有的图片使用的是相同的转换矩阵，这会导致坡度变化下的误差。

为了解决这个问题，论文训练了一个可以预测变换矩阵H的神经网络HNet，网络的输入是图片，输出是转置矩阵H。之前移植过Opencv逆透视变换矩阵的源码，里面转换矩阵需要8个参数，这儿只给了6个参数的自由度，一开始有些疑惑，后来仔细阅读paper，发现作者已经给出了解释，是为了对转换矩阵在水平方向上的变换进行约束。

代码分析

        binary_seg_image, instance_seg_image = sess.run(
            [binary_seg_ret, instance_seg_ret],
            feed_dict={input_tensor: [image]}
        )

输入(１,256,512,3)　输出binary_seg_image:(1, 256, 512) instance_seg_image:(1, 256, 512, 4)

完成像素级别的分类和向量表示

class LaneNet的inference分为两步．　　

第一步提取分割的特征,包括了用于语义分割的特征和用以实例分割的特征.

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
            )
            #得到一个字典,包含了用于语义分割的feature map和用于实例分割的feature map．
            #binary_segment_logits　(1,256,512,2) 2是类别数目.即车道/背景．
            #instance_segment_logits　(1,256,512,64) 用以后面再做卷积为每个像素生成一个向量表示
            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]})])

特征提取完毕,做后处理

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

对每个像素的分类,做softmax转成概率.再argmax求概率较大值的下标.　　对每个像素的向量表示,用1x1卷积核做卷积,得到channel维度=CFG.TRAIN.EMBEDDING_FEATS_DIMS(配置为4).即(1,256,512,64)卷积得到(1,256,512,4)的tensor．即每个像素用一个四维向量表示.

所以,整个LaneNet的inference返回的是两个tensor.一个shape为(1,256,512) 一个为(1,256,512,4).

后处理

class LaneNetPostProcessor(object):
    def postprocess(self, binary_seg_result, instance_seg_result=None,
                min_area_threshold=100, source_image=None,
                data_source='tusimple'):

对binary_seg_result,先通过形态学操作将小的空洞去除.参考https://www.cnblogs.com/sdu20112013/p/11672634.html

然后做聚类.

    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

获取到坐标及对应坐标像素对应的向量表示.

np.where(condition)

只有条件 (condition)，没有x和y，则输出满足条件 (即非0) 元素的坐标 (等价于numpy.nonzero)。这里的坐标以tuple的形式给出，通常原数组有多少维，输出的tuple中就包含几个数组，分别对应符合条件元素的各维坐标。

测试结果

tensorflow-gpu 1.15.2

4张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

类似于高吞吐量,高延迟.对单帧图片处理在1-2s,多幅图片同时处理,平均下来的处理速度在0.1s.

论文里的backbone为enet,在nvida 1080 ti上推理速度52fps.

对于这个问题的解释,作者的解释是

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:)

一个是特征提取网络和论文里不一致,一个是gpu有一个短暂的warm up的时间.

我自己的测试结果是在extract image features耗时较多.换一个backbone可能会有改善.

   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

参考:https://www.cnblogs.com/xuanyuyt/p/11523192.html　　https://zhuanlan.zhihu.com/p/93572094