Automatic Defect Detection of Pavement Diseases

Abstract

Pavement disease detection is an important task for ensuring road safety. Manual visual detection requires a significant amount of time and effort. Therefore, an automated road disease identification technique is required to guarantee that city tasks are performed. However, due to the irregular shape and large-scale differences in road diseases, as well as the imbalance between the foreground and background, the task is challenging. Because of this, we created the deep convolution neural network—DASNet, which can be used to identify road diseases automatically. The network employs deformable convolution instead of regular convolution as the feature pyramid’s input, adds the same supervision signal to the multi-scale features before feature fusion, decreases the semantic difference, extracts context information by residual feature enhancement, and reduces the information loss of the pyramid’s top-level feature map. Considering the unique shape of road diseases, imbalance problems between the foreground and background are common, therefore, we introduce the sample weighted loss function. In order to prove the superiority and effectiveness of this method, it is compared to the latest method. A large number of experiments show that this method is superior in accuracy to other methods, specifically, under the COCO evaluation metric, compared with the Faster RCNN baseline, the proposed method obtains a 41.1 mAP and 3.4 AP improvement.

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

“…Tang et al [33] proposed a new deep-learning framework called IOPLIN. Similarly, Zhao et al [34] proposed DASNet, a deep convolutional neural network, which can be used to automatically identify road diseases. The network uses demorphable convolution instead of conventional convolution as the input of the feature pyramid.…”

Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion

“…The mAP was improved by 4.1% compared to the base model. In addition, Zhao et al [ 22 ] optimized the Faster R-CNN by combining deformable convolution with a pyramid network as the backbone network, and the mAP of the model was improved by 3.4% compared to it before applying the optimization. Finally, Xiang et al [ 23 ] optimized Yolov5 by adding a transformer [ 24 ], which enhanced the model’s ability to extract the feature dependencies of the pavement cracks over a large range.…”

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

“…To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and computer vision (CV), have been introduced to enable autonomous SHM of various structures and infrastructure, including bridges (Mirzazade et al, 2021;Zoubir et al, 2022), pipelines (Y. Li et al, 2023;Ma et al, 2022), tunnel structures (Marasco et al, 2022;Rosso et al, 2023), and pavements (Rodriguez-Lozano et al, 2023;L. Zhao et al, 2022), among others (Ye et al, 2023).…”

“…Conventional methods encounter significant challenges in handling high‐dimensional and unstructured image data, whereas ML has demonstrated notable advantages in the domain of image recognition. Consequently, numerous ML methods have been proposed for vision‐based SHM tasks, including damage detection (Lin et al., 2022), nondestructive evaluation (Xiaofeng Li et al., 2022), deformation or displacement monitoring (J. Zhao et al., 2022; Zhuge et al., 2022), and 3D reconstruction (Wei et al., 2022). It is worth mentioning that the integration of advanced algorithms, such as ML, in vibration‐based SHM is also an active area of research.…”