Citation: Chen, G.-H.; Ni, J.; Chen, Z.;
Huang, H.; Sun, Y.-L.; Ip, W.H.; Yung,
K.L. Detection of Highway Pavement
Damage Based on a CNN Using
Grayscale and HOG Features. Sensors
2022, 22, 2455. https://doi.org/
10.3390/s22072455
Academic Editor: Guangtao Zhai
Received: 24 February 2022
Accepted: 18 March 2022
Published: 23 March 2022
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Communication
Detection of Highway Pavement Damage Based on a CNN
Using Grayscale and HOG Features
Guo-Hong Chen
1
, Jie Ni
1
, Zhuo Chen
1
, Hao Huang
2,3,
*, Yun-Lei Sun
1,
* , Wai Hung Ip
4
and Kai Leung Yung
4
1
School of Information and Electrical Engineering, Zhejiang University City College, 51 Huzhou Street,
Hangzhou 310015, China; chenguohong@zucc.edu.cn (G.-H.C.); nij@zucc.edu.cn (J.N.);
chenz@zucc.edu.cn (Z.C.)
2
Hubei Key Laboratory of Ferro- & Piezoelectric Materials and Devices, Faculty of Physics and
Electronic Science, Hubei University, 368 Youyi Street, Wuhan 430062, China
3
Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and
Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
4
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University,
Hong Kong, China; wh.ip@polyu.edu.hk (W.H.I.); kl.yung@polyu.edu.hk (K.L.Y.)
* Correspondence: haohuang@hubu.edu.cn (H.H.); sunyl@zucc.edu.cn (Y.-L.S.)
Abstract:
Aiming at the demand for rapid detection of highway pavement damage, many deep
learning methods based on convolutional neural networks (CNNs) have been developed. However,
CNN methods with raw image data require a high-performance hardware configuration and cost
machine time. To reduce machine time and to apply the detection methods in common scenarios, the
CNN structure with preprocessed image data needs to be simplified. In this work, a detection method
based on a CNN and the combination of the grayscale and histogram of oriented gradients (HOG)
features is proposed. First, the Gamma correction was employed to highlight the grayscale distribu-
tion of the damage area, which compresses the space of normal pavement. The preprocessed image
was then divided into several unit cells, whose grayscale and HOG were calculated, respectively.
The grayscale and HOG of each unit cell were combined to construct the grayscale-weighted HOG
(GHOG) feature patterns. These feature patterns were input to the CNN with a specific structure
and parameters. The trained indices suggested that the performance of the GHOG-based method
was significantly improved, compared with the traditional HOG-based method. Furthermore, the
GHOG-feature-based CNN technique exhibited flexibility and effectiveness under the same accuracy,
in comparison to those deep learning techniques that directly deal with raw data. Since the grayscale
has a definite physical meaning, the present detection method possesses a potential application for
the further detection of damage details in the future.
Keywords: pavement distress; feature combination; CNN
1. Introduction
As highway infrastructure is developing rapidly currently, the pavement management
standards generate the need for frequent inspection and maintenance. Distress detection
and pavement diagnosis of highway infrastructure are key to guaranteeing their permanent
availability. To improve the management of highways, it is efficient to carry out research
and extend fast detection techniques [1,2].
Since image sensor technology has been developing in recent years, imaging speed
has increased significantly, and imaging quality has also been improved. For instance,
a global shutter camera can realize fast imaging, which has evident advantages in cost
compared to ground penetrating radar or laser systems. This progress lays the foundation
for road assessment and diagnosis based on machine vision. Therefore, image recognition
technology for road damage can be achieved based on deep learning detection methods,
instead of traditional image processing methods [
3
–
5
]. In detail, the threshold technique,
contour detection, and frequency domain analysis, combined with specific geometric
Sensors 2022, 22, 2455. https://doi.org/10.3390/s22072455 https://www.mdpi.com/journal/sensors
