Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1139-1145.doi: 10.13229/j.cnki.jdxbgxb.20220126

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Depth estimation algorithm of monocular image based on self-supervised learning

Lin BAI1(),Lin-jun LIU1,Xuan-ang LI1,Sha WU1,Ru-qing LIU2   

  1. 1.School of Computer，Electronics and Information，Guangxi University，Nanning 530004，China
    2.College of Electronic Engineering，Guangxi Normal University，Guilin 541004，China
  • Received:2022-02-11 Online:2023-04-01 Published:2023-04-20

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Abstract:

In order to improve the effectiveness of monocular image depth estimation， a monocular image depth estimation algorithm based on self supervised learning is designed. The absolute depth feature， relative depth feature and position feature of monocular image are obtained， and the logarithm of depth difference is used as the loss function to obtain the point-to-point correlation feature of image； The surface normal loss function is used to deal with the surface normal of the depth map to reduce the surface fluctuation of the image， and the similarity between the image photometric loss function and the two images is calculated； The monocular image is input into the self supervised learning network， the depth amplitude is calculated， and the monocular image depth estimation based on self supervised learning is completed. The experiment is mainly divided into objective evaluation and subjective evaluation. In the objective evaluation part， the accuracy of depth prediction of the proposed estimation algorithm reaches 91.2%， and the edge error of depth map is only 3%. This method has high accuracy. In subjective evaluation， the proposed algorithm is verified to be able to predict the image features and improve the image depth estimation effect effectively.

Key words: software engineering, self-supervised learning, monocular image, depth estimation, feature extraction, loss function

CLC Number: 

  • TP311

Fig.1

Monocular image depth estimation processbased on self supervised learning"

Fig.2

Self supervised learning network trainingprocess"

Fig.3

Experimental image"

Fig.4

Comparison of depth prediction accuracy"

Fig.5

Comparison of accuracy of depth image edgeestimation"

Fig.6

Image 1 estimation results"

Fig.7

Image 2 estimation results"

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