Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features

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 distribution 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.

Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

“…A histogram of oriented gradients (HOG) is an older algorithm of object detection; however, it is still very popular for human recognition, see [5] or [6]. Unlike most modern algorithms, a HOG is based on feature detection rather than being trained as a neural network.…”

A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning

“…Representative studies include crack detection using deep learning techniques such as convolutional neural network (CNN) and "You Only Look Once (YOLO)" [11], future flatness prediction using the support vector machine model [12], and auto porthole detection using CNN [13]. There are cases of research using various image-based AI technologies such as fuzzy C-means, holistically nested edge detection, multi-layer perception, random forest classifier, support vector classifier, recurrent neural network (RNN), histogram of oriented gradients (HOG), and grayscaleweighted HOG [14][15][16][17][18][19][20][21][22]. Image-based research is also essential for automatic analysis processing; however, there were few cases of introducing the AI method for predicting the road pavement condition index used in decision-making.…”

Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

“…Chen et al put forward a fast pavement damage detection method based on the method that combines a CNN and directional gradient histogram to solve the problem of time-consuming calculation in the direct processing of original images by the deep learning method. The test shows that this method has better robustness than the method using a gray and directional gradient histogram, and improves the detection efficiency compared to the pure CNN method [21]. Mei and Gül proposed a Con-nCrack method combining conditional Wasserstein generative adversarial networks and connected graphs for road crack detection and used a motion camera mounted on the rear of a vehicle to acquire road images.…”