  Real-time semantic segmentation is a challenging task that requires
high-accuracy models with low-inference times. Implementing these models on
embedded systems is limited by hardware capability and memory usage, which
produces bottlenecks. We propose an efficient model for real-time semantic
segmentation called JetSeg, consisting of an encoder called JetNet, and an
improved RegSeg decoder. The JetNet is designed for GPU-Embedded Systems and
includes two main components: a new light-weight efficient block called
JetBlock, that reduces the number of parameters minimizing memory usage and
inference time without sacrificing accuracy; a new strategy that involves the
combination of asymmetric and non-asymmetric convolutions with
depthwise-dilated convolutions called JetConv, a channel shuffle operation,
light-weight activation functions, and a convenient number of group
convolutions for embedded systems, and an innovative loss function named
JetLoss, which integrates the Precision, Recall, and IoUB losses to improve
semantic segmentation and reduce computational complexity. Experiments
demonstrate that JetSeg is much faster on workstation devices and more suitable
for Low-Power GPU-Embedded Systems than existing state-of-the-art models for
real-time semantic segmentation. Our approach outperforms state-of-the-art
real-time encoder-decoder models by reducing 46.70M parameters and 5.14%
GFLOPs, which makes JetSeg up to 2x faster on the NVIDIA Titan RTX GPU and the
Jetson Xavier than other models. The JetSeg code is available at
https://github.com/mmontielpz/jetseg.
