# Lane Line Identification and Research Based on Markov Random Field

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Image Preprocessing

#### 2.1. Color Space Transformation

#### 2.2. Image Smoothing

#### 2.3. Grayscale Transformation

#### 2.4. Road Region of Interest Extraction

## 3. Markov Random Field

_{n}represents the category of the n pixel, and in the lane line detection task we specify that the categories are only lane lines and non-lane lines. W

_{S}represents the prior category of the image as a whole, and W

_{En}represents the category of the pixel n neighborhood range. P (Wn|Ws) represents the conditional probability that the class of pixel n is W

_{n}when the global class is W

_{S}. This feature explains the principle of conditional independence in undirected graph models, i.e., given a neighborhood, each pixel variable should be conditionally independent of each other.

## 4. MRF Reasoning Based on Graph Cut Method

#### 4.1. Maximum Posterior Probability Reasoning

#### 4.2. Binary Graph Cut Method

#### 4.3. Maximum Flow Problem

#### 4.4. Determination of Energy Potential Function

## 5. Experimental Results and Analysis

#### 5.1. Experimental Environment and Simulation

#### 5.2. Model Quantitative and Qualitative Analysis

_{pred}denotes the lane line prediction result and N

_{gt}denotes the lane line ground truth label.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**MDPI and ACS Style**

Ding, F.; Wang, A.; Zhang, Q.
Lane Line Identification and Research Based on Markov Random Field. *World Electr. Veh. J.* **2022**, *13*, 106.
https://doi.org/10.3390/wevj13060106

**AMA Style**

Ding F, Wang A, Zhang Q.
Lane Line Identification and Research Based on Markov Random Field. *World Electric Vehicle Journal*. 2022; 13(6):106.
https://doi.org/10.3390/wevj13060106

**Chicago/Turabian Style**

Ding, Fang, Aiguo Wang, and Qianbin Zhang.
2022. "Lane Line Identification and Research Based on Markov Random Field" *World Electric Vehicle Journal* 13, no. 6: 106.
https://doi.org/10.3390/wevj13060106