An Automatic Road Distress Visual Inspection System Using an Onboard In-Car Camera

Abstract

Speaking of road maintenance, the preventive maintenance strategy is preferable for most governments. Many governments possess special vehicles that can accurately detect and classify many types of road distresses. By running these vehicles frequently, small road distresses will be detected before growing into the big ones. However, because running these huge and expensive vehicles is not easy, in practical, it usually ends up with infrequent road inspection regardless of having automatic road inspection vehicles. In this paper, we focus on investigating and developing an automatic and nondestructive visual inspection system whose setup and usage are designed by considering the context of drivers, driving styles, and road conditions in Bangkok, the capital city of Thailand. Our proposal includes a workflow diagram of a vision-based road inspection system that is capable of detecting, classifying, tracking, measuring, and pricing road distresses. As for the proof-of-concept, our current system focuses on detecting one specific type of road distresses called pothole, using only one onboard in-car camera. Experimental results reveal that the context of Bangkok introduces many nontrivial challenges for vision-based analysis systems where maintaining both accuracy and ease of use altogether may not be easy.

A new feature extraction approach based on non linear source separation

“…For these sets, the supervised classification is operated and accuracies are performed. The union of PSS and SSS constitutes the source space as presented in (10). PSS are determined by (11) where C denotes the source combination set.…”

“…Features extraction approaches for land cover classification Spatial metrics and Texture measures for land cover objects classification [1] Parcels geometrical attributes including shape, height, proximity to major roads, similarity to neighbors [2] Spectral indices (NDVI, MNDWI, NDBI) [3] Object-based feature extraction based on spatial and spectral statistics [4] High dimensional feature vector combining focal textures statistics (median, mean, and standard deviation) and Gray Level Co-occurrence Matrix derived in different kernel sizes [5] Semantic features extraction using different deep convolutional neural networks models [6] High-level semantic features extraction based on transfer learning and the Inception-ResNet-v2 model [7] Deep semantic feature extraction using different models (VGG-S , VGG-M, VGG-F, VGG-VD16, VGG-VD16) [8] Combining deep semantic features, spectral features and GLCM texture features [9] Vision-based technology have been widely used for object of interest detection. For instance, in [10] the detecting task is based on histogram equalization and morphological processings. The method aimed to detect, classify and track road distresses.…”

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

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A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

“…A huge recent development was found with the employment of machine learning and deep learning techniques, which demonstrate higher classification accuracy and robustness. Both deep learning and computer vision methods [1]- [15] were developed in various vehicle-relevant applications, such as vehicle detection, vehicle classification, vehicle plate recognition, and road condition monitoring [16]. Comparing with computer vision methods, the newly developed deep learning techniques have some advantages on the generalization capacity and robustness to uncertainties, noise, and occlusion in images, in the cost of higher computation load and demand on sample set size.…”