  In this work we investigate the problem of road scene semantic segmentation
using Deconvolutional Networks (DNs). Several constraints limit the practical
performance of DNs in this context: firstly, the paucity of existing pixel-wise
labelled training data, and secondly, the memory constraints of embedded
hardware, which rule out the practical use of state-of-the-art DN architectures
such as fully convolutional networks (FCN). To address the first constraint, we
introduce a Multi-Domain Road Scene Semantic Segmentation (MDRS3) dataset,
aggregating data from six existing densely and sparsely labelled datasets for
training our models, and two existing, separate datasets for testing their
generalisation performance. We show that, while MDRS3 offers a greater volume
and variety of data, end-to-end training of a memory efficient DN does not
yield satisfactory performance. We propose a new training strategy to overcome
this, based on (i) the creation of a best-possible source network (S-Net) from
the aggregated data, ignoring time and memory constraints; and (ii) the
transfer of knowledge from S-Net to the memory-efficient target network
(T-Net). We evaluate different techniques for S-Net creation and T-Net
transferral, and demonstrate that training a constrained deconvolutional
network in this manner can unlock better performance than existing training
approaches. Specifically, we show that a target network can be trained to
achieve improved accuracy versus an FCN despite using less than 1\% of the
memory. We believe that our approach can be useful beyond automotive scenarios
where labelled data is similarly scarce or fragmented and where practical
constraints exist on the desired model size. We make available our network
models and aggregated multi-domain dataset for reproducibility.
