Pothole image detection based on YOLOv7 algorithm

Ronggui Ma; Xunyan Huang; Zizhong Wei; Xianglin Sun

doi:10.1117/12.2689209

31 July 2023 Pothole image detection based on YOLOv7 algorithm

Ronggui Ma, Xunyan Huang, Zizhong Wei, Xianglin Sun

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127471A (2023) https://doi.org/10.1117/12.2689209
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

As one of the common road diseases, the accurate detection of potholes during inspections can help to make timely maintenance measures, which will greatly save road maintenance costs and reduce the incidence of traffic accidents. In order to improve the accuracy and timeliness of pothole detection and to facilitate the development of disease maintenance, the YOLOv7 model is improved. Firstly, the Efficient convolution operator (DSConv) is introduced to reconstruct the Backbone and Head parts of YOLOv7 to reduce the computational effort of the original YOLOv7 model and improve the detection speed of the model. Secondly, the SE attention mechanism is incorporated into the model to improve the model's ability to extract features from potholes. Finally, the latest v3 version of Wise-IoU is introduced as the loss function of the improved model to reduce the impact caused by sample annotation. The accuracy and mAP of the improved model improved by 3.04% and 1.34% respectively compared to the original YOLOv7 model, and the number of FLOPS of the model decreased from 105.1 to 48.8. The results show that the proposed improved method can effectively improve the speed and accuracy of the YOLOv7 model in detecting potholes, and is advanced compared to the current mainstream target detection algorithms.

Citation Download Citation

Ronggui Ma, Xunyan Huang, Zizhong Wei, and Xianglin Sun "Pothole image detection based on YOLOv7 algorithm", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127471A (31 July 2023); https://doi.org/10.1117/12.2689209

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Roads

Convolution

Detection and tracking algorithms

Feature extraction

Target detection

Convolutional neural networks

Neural networks

Keywords/Phrases

Search In:

Publication Years