AI 中文总结
针对城市场景语义分割，EGRNet采用深度可分离卷积、扩张残差块、边缘门控细化模块和挤压激励注意力等方法，以低计算成本实现高精度，还引入对抗攻击检测策略，适合在边缘设备部署。
AI 中文摘要
随着自主系统和智慧城市不断发展，高效且强大的场景理解需求愈发关键。语义分割对自动驾驶车辆理解复杂城市环境至关重要，但以最小计算成本实现高精度仍是挑战。本文提出用于城市场景实时语义分割的轻量级高效深度学习模型EGRNet。它采用深度可分离卷积降低计算复杂度，用扩张残差块捕捉多尺度上下文信息，引入边缘门控细化模块自适应融合特征，还应用了挤压激励注意力。仅0.46M参数就实现了当前最优性能，在Cityscapes数据集上平均交并比达65.28%。此外还引入轻量级对抗攻击检测策略确保鲁棒性。EGRNet适合在安全关键实时应用的边缘设备上部署。
英文摘要
As autonomous systems and smart cities continue to evolve, the demand for efficient and robust scene understanding becomes increasingly critical. Semantic segmentation plays a key role in enabling autonomous vehicles to comprehend complex urban environments. However, achieving high accuracy with minimal computational cost remains a significant challenge. In this paper, we present Edge-Gated Refinement Network (EGRNet), a lightweight and efficient deep learning model designed for real-time semantic segmentation in urban scenarios. The model incorporates depthwise separable convolutions to reduce computational complexity and dilated residual blocks for capturing rich multi-scale contextual information. Additionally, we introduce a novel Edge-Gated Refinement (EGR) module, which adaptively fuses original and refined features through a learnable gating mechanism, enhancing boundary preservation and edge-sensitive regions. To further improve feature representation, Squeeze-and-Excitation (SE) attention is applied across the network. With only 0.46M parameters, EGRNet achieves state-of-the-art performance while maintaining low computational overhead. When evaluated on the Cityscapes dataset, the model attains a mean Intersection over Union (mIoU) of 65.28%, demonstrating strong accuracy with minimal resource consumption. Moreover, we introduce a lightweight adversarial attack detection strategy, ensuring robustness against adversarial inputs without compromising real-time performance. By combining efficiency, accuracy, and resilience, EGRNet is well-suited for deployment on edge devices in safety-critical real-time applications.
Comments14 pages, 8 figures, 3 tables and 1 algorithm
