A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes

Abstract

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time complexity while training them on manually labeled data. Yet, the unavailability of segmentation masks for host lanes in harsh road environments hinders fully supervised methods' operability on such a problem. This work proposes integrating traditional computer vision techniques and deep learning methods to develop a reliable benchmarking framework for lane detection tasks in complex and dynamic road scenes. Firstly, an automatic segmentation algorithm based on a sequence of traditional computer vision techniques has been experimented. This algorithm precisely segments the semantic region of the host lane in the complex urban images of nuScenes dataset used in this framework; hence corresponding weak labels are generated. After that, the developed data is qualitatively evaluated to be used in training and benchmarking five state-of-the-art FCN-based architectures: SegNet, Modified SegNet, U-Net, ResUNet, and ResUNet++. The performance evaluation of the trained models is done visually and quantitatively by considering lane detection a binary semantic segmentation task. The output results show robust performance, especially ResUNet++, which outperforms all the other models while testing them in different complex road scenes with dynamic scenarios and various lighting conditions.

Lane Detection in Autonomous Vehicles: A Systematic Review

“…However, the lack of segmentation masks for host lanes in adverse road environments limits the applicability of fully supervised algorithms to such a situation. To address this issue, Yousri et al [23] propose combining classical computer vision techniques and deep learning approaches to establish a reliable benchmarking framework for lane recognition tasks in complicated and dynamic road scenarios.…”

“…Meanwhile, several researchers have relied only on the public dataset for training and validation. In road lane marking, radio detection and ranging (radar), a camera, a global positioning system (GPS), and light detection and range (LiDAR) have all been used for the self-collect dataset [23]. Other than that, there are also data from the online simulator collected in various works of literature.…”

The Improved Deeplabv3plus Based Fast Lane Detection Method

“…With the improvement of human living standards and the high level of material life requirements, autonomous driving has attracted more and more attention. In the field of autonomous driving, an important problem is automatic lane detection, which is one of the most challenging perceptual tasks at present [1]. Detecting lanes is a subtask of advanced functions of autonomous driving, such as lane departure warning, Advanced Driving Assistance System (ADAS), lane-keeping, and path planning [2].…”

“…In [6], the Gaussian filter, improved Hough transform, and K-mean clustering algorithm were comprehensively carried out in the lane detection. These methods can reliably identify lanes in simple scenes, but they are possibly vulnerable to the shadows on the road and have poor accuracy in complex and variable driving scenes [1].…”

“…To our knowledge, many methods that combine traditional machine vision with deep learning are also available. Reference [1] proposed an adaptive and optimized machine vision method for lane segmentation, including color space transformation, perspective transformation, Hough transformation, lane fitting, and lane filling. The weak labels were then generated by a qualitative evaluation, which was used to train other advanced neural networks such as RESNET and SEGNET.…”