  Object detection and segmentation are two core modules of an autonomous
vehicle perception system. They should have high efficiency and low latency
while reducing computational complexity. Currently, the most commonly used
algorithms are based on deep neural networks, which guarantee high efficiency
but require high-performance computing platforms. In the case of autonomous
vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms
with limited computing power, which makes it difficult to meet the requirements
described above. A reduction in the complexity of the network can be achieved
by using an appropriate: architecture, representation (reduced numerical
precision, quantisation, pruning), and computing platform. In this paper, we
focus on the first factor - the use of so-called detection-segmentation
networks as a component of a perception system. We considered the task of
segmenting the drivable area and road markings in combination with the
detection of selected objects (pedestrians, traffic lights, and obstacles). We
compared the performance of three different architectures described in the
literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments
on a custom dataset consisting of approximately 500 images of the drivable area
and lane markings, and 250 images of detected objects. Of the three methods
analysed, MultiTask V3 proved to be the best, achieving 99% mAP_50 for
detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane
segmentation, as well as 124 fps on the RTX 3060 graphics card. This
architecture is a good solution for embedded perception systems for autonomous
vehicles. The code is available at: https://github.com/vision-agh/MMAR_2023.
