ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation
This addresses lane segmentation for autonomous driving systems, but it is incremental as it builds on existing CNN methods with novel feature learning components.
The paper tackles lane segmentation in autonomous driving by proposing ContinuityLearner, a CNN-based network that learns geometric continuity features of lanes, achieving superior performance on the CULane and Tusimple benchmarks compared to state-of-the-art methods.
Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects. To empower conventional CNNs in learning geometric clues of lanes, we propose a deep network named ContinuityLearner to better learn geometric prior within lane. Specifically, our proposed CNN-based paradigm involves a novel Context-encoding image feature learning network to generate class-dependent image feature maps and a new encoding layer to exploit the geometric continuity feature representation by fusing both spatial and visual information of lane together. The ContinuityLearner, performing on the geometric continuity feature of lanes, is trained to directly predict the lane in traffic scenarios with integrated and continuous instance semantic. The experimental results on the CULane dataset and the Tusimple benchmark demonstrate that our ContinuityLearner has superior performance over other state-of-the-art techniques in lane segmentation.