A Deeply Supervised Semantic Segmentation Method Based on GAN
Wei Zhao Qiyu Wei Zeng Zeng
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.