CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

Understand

Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies.

• In this work, we propose CondLaneNet, a novel top-to-down lane detection framework that detects the lane instances first and then dynamically predicts the line shape for each instance.
• Aiming to resolve lane instance-level discrimination problem, we introduce a conditional lane detection strategy based on conditional convolution and row-wise formulation.
• Further, we design the Recurrent Instance Module(RIM) to overcome the problem of detecting lane lines with complex topologies such as dense lines and fork lines.