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.

翻译：近年来，随着交通系统对自动化和效率需求的日益增长，智能交通领域取得了快速发展。作为智能交通系统的核心任务之一，交通安全要求精确识别并定位道路裂缝、车道线和交通标志等各类道路元素。语义分割通过将图像分割为具有精确边界的语义区域，在实现该任务中发挥着关键作用。本研究提出一种改进的语义分割模型，该模型融合了对抗学习与最先进语义分割技术的优势。所提模型将生成对抗网络（GAN）框架整合到传统语义分割模型中，增强了模型捕捉交通图像中复杂细微特征的能力。与现有方法（即SEGAN）相比，我们的方法在道路裂缝数据集上的性能显著提升，验证了其有效性。这一改进归因于对抗学习与语义分割的协同效应，从而实现了道路结构与状态的更精细、更准确表征。改进后的模型不仅有助于更准确地检测道路裂缝，还可广泛应用于智能交通领域，如交通标志识别、车辆检测和车道分割等任务。