An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region\u27s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera\u27s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ( presence/absence detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating

Pavement Defect Classification and Localization Using Hybrid Weakly Supervised and Supervised Deep Learning and GIS

Automated detection of road defects has historically been challenging for the pavement management industry. As a result, new methods have been developed over the past few years to handle this issue. Most of these methods relied on supervised machine learning techniques, such as object detection and segmentation methods, which need a large, annotated image dataset to train their models. However, annotating pavement defects is difficult and time-consuming due to their ununiformed and complex shapes. To address this challenge, a hybrid pavement defect classification and localization framework using weakly supervised and supervised deep learning methods is proposed in this thesis. This framework has two steps: (1) A robust hierarchical two-level classifier that classifies the defects in images, and (2) A method for defect localization combining weakly supervised and supervised techniques. In the localization method, first, defects are primarily localized using a weakly supervised method (i.e. Class Activation Mapping (CAM)). Next, based on the results of the first classifiers, the defects are segmented from the localized patches obtained in the previous step. The feature maps extracted from the CAM method are used to train a segmentation network once (i.e. U-Net or Mask R-CNN) to localize and segment the defects in the images. Thus, the proposed framework combines the advantages of weakly supervised and supervised methods. The supervised modules in the framework are trained once and can be used for any new data without the need to train. In other words, to use our framework on new dataset only the classifiers should be fine-tuned. Furthermore, the proposed framework introduced an innovative method designed to calculate the maximum crack width in pixels within linear segmented defect patches, derived from the localization module of the proposed framework. This method is particularly advantageous as it provides critical information that can be further employed in the calculation of the Pavement Condition Index (PCI). Additionally, the proposed method benefits from an asset management inspection system based on Geographic Information System (GIS) technology to prepare the dataset used in the training and testing. Thus, this advanced system serves a dual role within our framework. Firstly, it assists in the assembly and preparation of the dataset used in the model training process, providing a geographically organized collection of images and related data. Secondly, it plays a crucial role in the testing phase, offering a spatially accurate platform for evaluating the effectiveness of the model in real-world scenarios. A dataset from Georgia State in the USA was used in the case study. The proposed framework obtained high precision of 97%, 88%, 92% and 97% for localizing the alligator, block, longitudinal and transverse cracks, respectively. Considering all factors, such as annotation cost, and performance on the test dataset, the proposed localization method outperforms the supervised localization methods, such as instance segmentation and object detection for localizing road pavement defect

Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model

Cracks on concrete surface are typically clear warning signs of a potential threat to the integrity and serviceability of structure. The techniques based on image processing can effectively detect the cracks from images. These techniques, however, are generally susceptible to user-driven heuristic thresholds and extraneous distractors. Inspired by recent success of artificial intelligence, a deep learning based automated crack detection system called CrackSN was developed. An image dataset of concrete surface is collected by smartphone and carefully prepared in order to develop and train the CrackSN system. This proposed deep learning model, built on the Adam-SqueezeNet architecture, automatically learns the discriminative feature directly from the labeled and augmented patches. Hyperparameters of SqueezeNet are tuned with Adam optimization additive through the training and validation procedures. The fine-tuned CrackSN model outperforms state-of-the-art models in recent literature by correctly classifying 97.3% of the cracked patches in the image dataset. The success of CrackSN model demonstrated with light network design and outstanding performance provides a key step toward automated damage inspection and health evaluation for infrastructure. &nbsp