88,883 research outputs found
A Deeply Supervised Semantic Segmentation Method Based on GAN
In recent years, the field of intelligent transportation has witnessed rapid
advancements, driven by the increasing demand for automation and efficiency in
transportation systems. Traffic safety, one of the tasks integral to
intelligent transport systems, requires accurately identifying and locating
various road elements, such as road cracks, lanes, and traffic signs. Semantic
segmentation plays a pivotal role in achieving this task, as it enables the
partition of images into meaningful regions with accurate boundaries. In this
study, we propose an improved semantic segmentation model that combines the
strengths of adversarial learning with state-of-the-art semantic segmentation
techniques. The proposed model integrates a generative adversarial network
(GAN) framework into the traditional semantic segmentation model, enhancing the
model's performance in capturing complex and subtle features in transportation
images. The effectiveness of our approach is demonstrated by a significant
boost in performance on the road crack dataset compared to the existing
methods, \textit{i.e.,} SEGAN. This improvement can be attributed to the
synergistic effect of adversarial learning and semantic segmentation, which
leads to a more refined and accurate representation of road structures and
conditions. The enhanced model not only contributes to better detection of road
cracks but also to a wide range of applications in intelligent transportation,
such as traffic sign recognition, vehicle detection, and lane segmentation.Comment: 6 pages, 2 figures, ITSC conferenc
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Ontology based Scene Creation for the Development of Automated Vehicles
The introduction of automated vehicles without permanent human supervision
demands a functional system description, including functional system boundaries
and a comprehensive safety analysis. These inputs to the technical development
can be identified and analyzed by a scenario-based approach. Furthermore, to
establish an economical test and release process, a large number of scenarios
must be identified to obtain meaningful test results. Experts are doing well to
identify scenarios that are difficult to handle or unlikely to happen. However,
experts are unlikely to identify all scenarios possible based on the knowledge
they have on hand. Expert knowledge modeled for computer aided processing may
help for the purpose of providing a wide range of scenarios. This contribution
reviews ontologies as knowledge-based systems in the field of automated
vehicles, and proposes a generation of traffic scenes in natural language as a
basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10
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