Automatic Pavement Crack Detection Based on Octave Convolution Neural Network with Hierarchical Feature Learning

Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.

Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.