SMPPALD—Segmentation Mask Post-Processing Algorithm for Improved Lane Detection

TL;DR

This paper introduces a new algorithm that improves lane detection in self-driving cars by refining neural network outputs.

Contribution

A novel post-processing algorithm for lane detection that improves accuracy using geometric and contextual rules.

Findings

SMPPALD improved F1 measure over SCNN on TuSimple and LLAMAS datasets.

The algorithm outperformed SCNN in most categories on the CULane dataset.

Abstract

As modern Advanced Driver Assistance Systems become increasingly prevalent in the automotive industry, Lane Detection (LD) solutions play a key role in enabling vehicles to drive autonomously or provide assistance to the driver. Many modern LD algorithms are based on neural networks, which estimate the locations of lane markings as segmentation masks in the input image. In this paper, we propose a novel algorithm, named SMPPALD (Segmentation Mask Post-Processing Algorithm for improved Lane Detection), designed to perform a set of post-processing operations on these segmentation masks to produce a list of points that define the lane markings. These operations follow geometric and contextual rules, taking into account the LD problem and improving detection accuracy. The algorithm was tested using the well-known and widely used Spatial Convolutional Neural Network (SCNN) on three different datasets (CULane, TuSimple, and LLAMAS). SMPPALD achieved a significant improvement in terms of F1 measure compared to SCNN on the TuSimple and LLAMAS datasets, while for the CULane dataset, it outperformed SCNN in most categories.

Figures