  For the self-driving and automatic parking, perception is the basic and
critical technique, moreover, the detection of lane markings and parking slots
is an important part of visual perception. In this paper, we use the semantic
segmentation method to segment the area and classify the class of lane makings
and parking slots on panoramic surround view (PSV) dataset. We propose the
DFNet and make two main contributions, one is dynamic loss weights, and the
other is residual fusion block (RFB). Dynamic loss weights are varying from
classes, calculated according to the pixel number of each class in a batch. RFB
is composed of two convolutional layers, one pooling layer, and a fusion layer
to combine the feature maps by pixel multiplication. We evaluate our method on
PSV dataset, and achieve an advanced result.
