  Convolutional neural networks (CNNs) are usually built by stacking
convolutional operations layer-by-layer. Although CNN has shown strong
capability to extract semantics from raw pixels, its capacity to capture
spatial relationships of pixels across rows and columns of an image is not
fully explored. These relationships are important to learn semantic objects
with strong shape priors but weak appearance coherences, such as traffic lanes,
which are often occluded or not even painted on the road surface as shown in
Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes
traditional deep layer-by-layer convolutions to slice-byslice convolutions
within feature maps, thus enabling message passings between pixels across rows
and columns in a layer. Such SCNN is particular suitable for long continuous
shape structure or large objects, with strong spatial relationship but less
appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a
newly released very challenging traffic lane detection dataset and Cityscapse
dataset. The results show that SCNN could learn the spatial relationship for
structure output and significantly improves the performance. We show that SCNN
outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet)
in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN
won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an
accuracy of 96.53%.
