Road Pavement Crack Detection Using Deep Learning with Synthetic Data

Robust automatic pavement crack detection is critical to automated road condition evaluation. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this process. This study makes literature review of detection issues of road pavement's distress. The paper considers the existing datasets for detection and segmentation distress of road and asphalt pavement. The work presented in this article focuses on deep learning approach based on synthetic training data generation for segmentation of cracks in the driver-view image. A synthetic dataset generation method is presented, and effectiveness of its applicability to the current problem is evaluated. The relevance of the study is emphasized by research on pixel-level automatic damage detection remains a challenging problem, due to heterogeneous pixel intensity, complex crack topology, poor illumination condition, and noisy texture background

Deep Learning Framework For Intelligent Pavement Condition Rating: A direct classification approach for regional and local roads

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter

An Optimized Deep Learning Framework For Pothole Detection

Pothole detection plays a crucial role in preventing road accidents and is effective in establishing road maintenance and safety. Although various pothole detection models are designed to accurately identify the pothole based on road images, they face issues in accuracy and hyperparameter tuning. The presented research work concentrates on developing a novel optimized deep learning model for the accurate prediction of potholes on the road infrastructure using the recurrent neural network (RNN) and grey wolf optimization (GWO). Initially, the road images are collected and pre-processed. The pre-processing includes the removal of noises, image resizing, etc., to improve the image quality. Further, texture-based feature extraction was employed to extract the most relevant features from the pre-processed image. Then, the RNN architecture was trained using the extracted features to learn the interconnections between the image features and pothole detection. In addition, the GWO fitness solution was integrated into the classification module to tune and optimize the RNN hyperparameters, which increases the detection performances such as accuracy, and reduces the loss function. Finally, the presented model was evaluated with the publically available road image detection database and the outcomes are determined. The performance assessment demonstrates that the designed model attained greater accuracy of 98.76%, and a loss function of 0.06. Furthermore, a comparative assessment was performed with existing methods to evaluate the effectiveness of the proposed model

Road pavement crack detection using deep learning with synthetic data

The improvement of road system quality is a critical task. The mechanism to address such important issue is close monitoring of road pavement condition. Traditional approach requires manual identification of damages. Taking into account considerable length of road system it is essential to create an effective automatic pavement defects detection tool. This approach will extremely reduce time for monitoring of current road state. In this paper global experience in solution of detection issues of road pavement's distress is reviewed. The article includes information about the existing datasets of road defects, which are commonly used for detection and segmentation. The present work is based on deep learning approach with the use of synthetic generated training data for segmentation of cracks in driver-view image. The novelty of the approach lies in creating synthetic dataset for training state-of-the-art deep learning frameworks. The relevance of the research is emphasized by processing of wide-view images in which heterogeneous pixel intensity, complex crack topology, different illumination condition and complexity of background make the task challenging