Abstract
In the process of road pavement health and safety assessment, crack detection plays a pivotal role in a preventive maintenance strategy. Recently, Convolutional Neural Networks (CNNs) have been applied to automatically identify the cracks on concrete pavements. The effectiveness of a CNN-based road crack detection and measurement method depends on several factors, including the image segmentation of cracks with complex topology, the inference of noises with similar texture to the distress, and the sensitivity to thin cracks. The presence of shadows, strong light reflections, and road markings can also severely affect the accuracy in detection and measurement. In this study, a review of the state-of-the-art CNN methods for crack identification is presented, paying attention to existing limitations. Then, a novel deep residual convolutional neural network (Parallel ResNet) is proposed with the aim of creating a high-performance pavement crack detection and measurement system. The challenge and special feature of Parallel ResNet is to remove the noise inference, identifying even thin and complex cracks correctly. The performance of Parallel ResNet has been investigated on two publicly available datasets (CrackTree200 and CFD), comparing it with that of competing methods suggested in the literature. Parallel ResNet reached the maximum scores in Precision (94.27%), Recall (92.52%), and F1 (93.08%) using the CrackTree200 dataset. Similarly, for the CFD dataset the novel method achieved high values in Precision (96.21%), Recall (95.12%), and F1 (95.63%). Based on the crack detection and image recognition results, mathematical morphology was then used to further minimize noise and accurately segment the road diseases, obtaining the outer contours of the connected domain in crack images. Therefore, crack skeletons have been extracted to measure the distress length, width, and area on images of rigid pavements. The experimental results show that Parallel ResNet can effectively minimize noise to obtain the geometry of cracks. The results of crack characteristic measurements are accurate and Parallel ResNet can be assumed as a reliable method in pavement crack image analysis, in order to plan the best road maintenance strategy.
Highlights
Monitoring, measuring, and evaluating pavement conditions are essential parts of road pavement maintenance activities, due to the perspective to plan corrective actions [1,2].Cracking is one of the most common road diseases [3,4]
Based on the convolutional layer parallel structure of the concept-v3 model [12,54], this paper proposes a network structure with a multiple skip connection convolutional layer connected in parallel, i.e., the Parallel Residual Network (ResNet) Module (Figure 4)
In order to validate Parallel ResNet as a reliable method in pavement crack image analysis, it was compared with six other competing methods defined in the literature, namely Canny, CrackForest, Modified VGG16, U-Net, Structured Prediction, and Ensemble
Summary
Monitoring, measuring, and evaluating pavement conditions are essential parts of road pavement maintenance activities, due to the perspective to plan corrective actions [1,2].Cracking is one of the most common road diseases [3,4]. Monitoring, measuring, and evaluating pavement conditions are essential parts of road pavement maintenance activities, due to the perspective to plan corrective actions [1,2]. The identification of cracks is a highly attractive problem as it allows the planning of the most efficient preventive maintenance interventions; cracks activate the main degradation phenomena of the pavement [5,6]. Sustainability 2022, 14, 1825 of severity found during the inspection allows the assessment of the level of pavement decay. The commonly used Pavement Condition Index (PCI) [7] provides a global assessment of flexible or rigid pavements by distinguishing the extent and severity of each defect. Traditional road crack detection methods are time consuming, wasteful, and subjective [8,9,10]. The road managers can prioritize and plan the maintenance of the road network, in order to keep the infrastructure in good condition and to extend their service life [14]
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