Lane Detection Based on Adaptive Network of Receptive Field

Yufan Cai,Yanyan Zhang,Chengsheng Pan,Mamoun Alazab

doi:10.1155/2021/6618459

Yufan Cai, Yanyan Zhang + Show 2 more

Open Access

https://doi.org/10.1155/2021/6618459

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Abstract

The difficulty of lane detection lies in the imbalance of the number of target pixels and background pixels. The sparse target distribution misleads the neural network to pay more attention to background segmentation in order to obtain a better loss convergence result. This makes it difficult for some models to detect lane line pixels and leads to the training fail (unable to output useful lane information). Increasing receptive field properly can enlarge the sphere of action between pixels, so as to restrain this trouble. Moreover, the interference information and noise existing in the real environment increase the difficulty of lane classification, such as vehicle occlusion, car glass reflection, and tree shadow. In this paper, we do think that the features obtained by the reasonable combination of receptive fields can help avoid oversegmentation of the image, so that most of the interference information can be filtered out. Based on this idea, Adaptive Receptive Field Net (ARFNet) is proposed to solve the problem of receptive field combination with the help of multireceptive field aggregation layers and scoring mechanism. This paper explains the working principle of ARFNet and analyzes several results of experiments, which are carried out to adjust network structure parameters in order to get better effects in the CuLane dataset testing.

Highlights

With the advent of 5G era, automatic driving as a new traffic concept has been developed rapidly
Considering the limitation of hardware, we only test some lightweight SOTA models: ResNet-34 [30, 31], SCNN [2]. e experimental result is shown in Table and the comparison of renderings with SCNN is shown in Figure
According to the comparative analysis of the renderings, we have found problems that Adaptive Receptive Field Net (ARFNet) shows typical attention preference. ough it can well judge the obvious lane line pixels, it is extremely sensitive to occlusions. e network tends to distinguish the covering part as background and only segments those pixels with distinct lane characteristics, which reflects that the robustness and the reasoning ability of the network for global context are still defective

Summary

Introduction

With the advent of 5G era, automatic driving as a new traffic concept has been developed rapidly. The widespread interference information in the driving environment makes it difficult to distinguish lane line pixels from background pixels To solve these problems, Lee et al [1] chose to engage more human interventions in the training process, using vanishing points to guide the training of deep models, but this way requires more manpower. Pan et al [2] added a spatial information transfer module in VGG16 [3] network to effectively resist the sparsity effect, and the lane position can be located more accurately by transferring the local information to the global This message passing process increases the inference time greatly. We have done some detailed adaptive ability exploration experiments and achieved good results in lane detection

Related Work

Methodology

Evaluation

Module Optimization Experiment

Structural Test

Conclusion