ENet-21: An Optimized light CNN Structure for Lane Detection

TL;DR

This paper introduces ENet-21, a lightweight CNN architecture optimized for lane detection in autonomous vehicles, capable of handling varying lane scenarios with improved efficiency and accuracy.

Contribution

It proposes a novel, less complex CNN structure that combines binary segmentation and Affinity Fields to detect multiple lanes and lane changes effectively.

Findings

Supports effectiveness on TuSimple dataset

Handles varying numbers of lanes and lane changes

Uses less complex CNN architecture

Abstract

Lane detection for autonomous vehicles is an important concept, yet it is a challenging issue of driver assistance systems in modern vehicles. The emergence of deep learning leads to significant progress in self-driving cars. Conventional deep learning-based methods handle lane detection problems as a binary segmentation task and determine whether a pixel belongs to a line. These methods rely on the assumption of a fixed number of lanes, which does not always work. This study aims to develop an optimal structure for the lane detection problem, offering a promising solution for driver assistance features in modern vehicles by utilizing a machine learning method consisting of binary segmentation and Affinity Fields that can manage varying numbers of lanes and lane change scenarios. In this approach, the Convolutional Neural Network (CNN), is selected as a feature extractor, and the final output is obtained through clustering of the semantic segmentation and Affinity Field outputs. Our method uses less complex CNN architecture than existing ones. Experiments on the TuSimple dataset support the effectiveness of the proposed method.