Deep Feature Fusion Network for Lane Line Segmentation in Urban Traffic Scenes

As autonomous driving technology continues to advance at a rapid pace, the demand for precise and dependable lane detection systems has become increasingly critical. However, traditional methods often struggle with complex urban scenarios, such as crowded environments, diverse lighting conditions, unmarked lanes, curved night-time driving. This paper presents novel approach line segmentation in traffic scenes Deep Feature Fusion Network (DFFN). The DFFN leverages strengths of deep learning feature extraction fusion, aiming enhance accuracy reliability under real-world conditions. To integrate multi-layer features, employs both spatial channel attention mechanisms an appropriate manner. strategy facilitates predicting relevance each input during fusion process. In addition, deformable convolution is employed all up-sampling operations, enabling dynamic adjustment receptive field according object scales poses. performance rigorously evaluated compared existing models, namely SCNN, ENet, ENet-SAD, across different scenarios CULane dataset. Experimental results demonstrate superior highlighting its potential applicability advanced driver assistance applications.