An improved real-time model for divergent and dense lane detection of unmanned vehicles

Abstract

Lane detection is the key technology of an advanced driver assistance system. This study proposed an efficient and novel lane detection method, Lane Kernel Parameter Module ResNet-18 (LKPM-ResNet-18), to improve the detection efficiency of lane with complex topologies (such as divergent lines and dense lines). This method can precisely predict the global structure in a bottom-up manner and directly output the curve parameters of the lane alignment model. In addition, LKPM was used to obtain accurately the local structure information and the slender shape of the lane with complex topology, and then the shape convolution kernel parameters of each instance are dynamically predicted to optimize the results of the subsequent output curves. Finally, the prior information of the global lane is fully integrated through the lane structure constraint loss, and the optimization results are fused into the global geometric curve fitting output, so that the fitted lanes are smoother and more continuous. The method proposed has real-time detection efficiency and requires little postprocessing because of the end-to-end direct approach. The validated results show that the efficiency of this method is obviously better than that of State-Of-The-Art (SOTA) methods, such as PointLaneNet and ENet-SAD. The F1 scores increase by 0.43% and 4.92%, respectively compared with the latest CurveLanes-NAS-L and SCNN on the CULane test dataset. The accuracy of 88.98% can be obtained on the CurveLanes test dataset. Meanwhile, the result from real driving environment detection exceeds the top performances of the other by 2.22% with the accuracy of 86.47%.