A Deeply Supervised Convolutional Neural Network for Pavement Crack Detection With Multiscale Feature Fusion

Qu, Zhong; Cao, Chong; Liu, Ling; Dong-yang, Zhou

doi:10.1109/tnnls.2021.3062070

Cited by 73 publications

(24 citation statements)

References 34 publications

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“…They used a VGG [97] like architecture and, similar to the basic FCN [88], upscaled and fused the feature maps to generate the output map with success. Qu et al [98] propose encoder-decoder architecture that makes extensive use of deep supervision. Their approach uses the DeepLab [99] segmentation architecture as a feature extractor and a novel multi-scale feature fusion which poses to be effective as features from deeper layers are effectively incorporated into the final segmentation output.…”

Section: A Supervised Learningmentioning

confidence: 99%

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

König¹,

Jenkins²,

Mannion³

et al. 2022

Preprint

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Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.

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Section: A Supervised Learningmentioning

confidence: 99%

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

König¹,

Jenkins²,

Mannion³

et al. 2022

Preprint

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show abstract

“…Zhou et al [36] propose a novel network architecture with richer feature fusion and attention mechanism and mixed pooling module for crack detection. Qu et al [37] propose a deeply supervised convolutional neural network for crack detection via a novel multiscale convolutional feature fusion module. A more fine-grained method is utilized in [38], where raw images are cropped into smaller images, and cracks are detected with a trained CNN classifier and an exhaustive search with a sliding window.…”

Section: Related Work 21 Crack Detectionmentioning

confidence: 99%

TransMF: Transformer-Based Multi-Scale Fusion Model for Crack Detection

Zhao

Qian

2022

Mathematics

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Cracks are widespread in infrastructure that are closely related to human activity. It is very popular to use artificial intelligence to detect cracks intelligently, which is known as crack detection. The noise in the background of crack images, discontinuity of cracks and other problems make the crack detection task a huge challenge. Although many approaches have been proposed, there are still two challenges: (1) cracks are long and complex in shape, making it difficult to capture long-range continuity; (2) most of the images in the crack dataset have noise, and it is difficult to detect only the cracks and ignore the noise. In this paper, we propose a novel method called Transformer-based Multi-scale Fusion Model (TransMF) for crack detection, including an Encoder Module (EM), Decoder Module (DM) and Fusion Module (FM). The Encoder Module uses a hybrid of convolution blocks and Swin Transformer block to model the long-range dependencies of different parts in a crack image from a local and global perspective. The Decoder Module is designed with symmetrical structure to the Encoder Module. In the Fusion Module, the output in each layer with unique scales of Encoder Module and Decoder Module are fused in the form of convolution, which can release the effect of background noise and strengthen the correlations between relevant context in order to enhance the crack detection. Finally, the output of each layer of the Fusion Module is concatenated to achieve the purpose of crack detection. Extensive experiments on three benchmark datasets (CrackLS315, CRKWH100 and DeepCrack) demonstrate that the proposed TransMF in this paper exceeds the best performance of present baselines.

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“…Yang et al [69] proposed an Enhanced Multiscale Feature Fusion Network, which combined task-wise attention and part complementary learning to extract and fuse the features, and utilize PMN to blend large-scale, middle-scale, and small-scale features in parallel during the three stages, make feature fusion can occur at different scales in all stages. Qu et al [70] on the basis of FPN, proposed a multiscale convolutional feature fusion. Huang et al [71] proposed a denoising-based multiscale feature fusion mechanism, the proposed DMSFF mechanism can aggregates multiscale features with the denoising operation at the stage of visual feature extraction.…”

Section: Related Work (I)mentioning

confidence: 99%

Lightweight Channel Attention and Multiscale Feature Fusion Discrimination for Remote Sensing Scene Classification

et al. 2021

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High-resolution remote sensing image scene classification has attracted widespread attention as a basic earth observation task. Remote sensing scene classification aims to assign specific semantic labels to remote sensing scene images to serve specified applications. Convolutional neural networks are widely used for remote sensing image classification due to their powerful feature extraction capabilities. However, the existing methods have not overcome the difficulties of large-scene remote sensing images of large intraclass diversity and high interclass similarity, resulting in low performance. Therefore, we propose a new remote sensing scene classification method that combines lightweight channel attention and multiscale feature fusion discrimination, called LmNet. First, ResNeXt is used as the backbone; second, a new lightweight channel attention mechanism is constructed to quickly and adaptively learn the salient features of important channels. Furthermore, we designed a multiscale feature fusion discrimination framework, which fully integrates shallow edge feature information and deep semantic information to enhance feature representation capabilities and uses multiscale features for joint discrimination. Finally, a cross-entropy loss function based on label smoothing is built to reduce the influence of interclass similarity on feature representation. In particular, our lightweight channel attention and multiscale feature fusion mechanism can be flexibly embedded in any advanced backbone as a functional module. The experimental results on three large-scale remote sensing scene classification datasets show that compared with the existing advanced methods, our proposed high-efficiency end-to-end scene classification method has reached state-of-the-art. Moreover, our method has a weaker dependence on labeled data and provided better generalization performance.INDEX TERMS Remote sensing scene classification, convolutional neural network, lightweight channel attention, multiscale feature fusion, label smoothing.

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A Deeply Supervised Convolutional Neural Network for Pavement Crack Detection With Multiscale Feature Fusion

Cited by 73 publications

References 34 publications

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

TransMF: Transformer-Based Multi-Scale Fusion Model for Crack Detection

Lightweight Channel Attention and Multiscale Feature Fusion Discrimination for Remote Sensing Scene Classification

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