A deeper generative adversarial network for grooved cement concrete pavement crack detection

Zhong, Jingtao; Huo, Ju; Zhang, Weiguang; Cheng, Hanglin; Zhang, Jing; Zheng, Tong; Jiang, Xi; Huang, Baoshan

doi:10.1016/j.engappai.2022.105808

Cited by 40 publications

(4 citation statements)

References 24 publications

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“…Thus, it is imperative to promptly recognize and rectify these issues. Timely detection and repair are paramount, with real-time identification of pavement distress proving particularly worthwhile [3,4]. Once information on urban pavement distress is gathered, specific maintenance tasks can be promptly executed, ensuring pavements fulfill their designated functions within their intended service life.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Xu,

Fu,

et al. 2024

Buildings

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Pavement distress seriously affects the quality of pavement and reduces driving comfort and safety. The dropped objects from vehicles have increased the risks of traffic accidents. Therefore, automatic detection of urban pavement distress and dropped objects is an effective method to timely evaluate pavement condition. Firstly, this paper utilized a portable platform to collect pavement distress and dropped objects to establish a high-quality dataset. Six types of pavement distresses: transverse crack, longitudinal crack, alligator crack, oblique crack, potholes, and repair, and three types of dropped objects: plastic bottle, metal bottle, and tetra pak were included in this comprehensive dataset. Secondly, the real-time YOLO series detection models were used to classify and localize the pavement distresses and dropped objects. In addition, segmentation models W-segnet, U-Net, and SegNet were utilized to achieve pixel-level detection of pavement distress and dropped objects. The results show that YOLOv8 outperformed YOLOv5 and YOLOv7 with a MAP of 0.889. W-segnet showed an overall MIoU of 70.65% and 68.33% on the training set and test set, respectively, being superior to the comparison model and being able to achieve high-precision pixel-level segmentation. Finally, the trained models were performed on the holdout dataset for the generalization test. The proposed methods integrated the detection of urban pavement distress and dropped objects, which could significantly contribute to driving safety.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Xu,

Fu,

et al. 2024

Buildings

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show abstract

“…Nevertheless, additional effort is required for improving the detection accuracy as it lacks the global perception of the cracks. Similarly, Zhong et al 24 developed an algorithm for generating synthetic images of grooved concrete pavement cracks using a deeper generative adversarial network. Additionally, they utilized U-Net and W-Segnet for achieving pixel-level crack detection.…”

Section: Introductionmentioning

confidence: 99%

A robust self-supervised approach for fine-grained crack detection in concrete structures

Sohaib,

Hasan,

Shah

et al. 2024

Sci Rep

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This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

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“…Crack is one of the common defects in infrastructure such as roads, bridges, and tunnels. Regular maintenance contributes to effectively extending the service life of these infrastructures [1][2][3]. Automatic crack detection based on computer vision has become an instantly efficient and widely adopted method due to its non-contact and cost-effectiveness [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation

Liang,

Li,

et al. 2024

Entropy

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Automatic crack segmentation plays an essential role in maintaining the structural health of buildings and infrastructure. Despite the success in fully supervised crack segmentation, the costly pixel-level annotation restricts its application, leading to increased exploration in weakly supervised crack segmentation (WSCS). However, WSCS methods inevitably bring in noisy pseudo-labels, which results in large fluctuations. To address this problem, we propose a novel confidence-aware co-training (CAC) framework for WSCS. This framework aims to iteratively refine pseudo-labels, facilitating the learning of a more robust segmentation model. Specifically, a co-training mechanism is designed and constructs two collaborative networks to learn uncertain crack pixels, from easy to hard. Moreover, the dynamic division strategy is designed to divide the pseudo-labels based on the crack confidence score. Among them, the high-confidence pseudo-labels are utilized to optimize the initialization parameters for the collaborative network, while low-confidence pseudo-labels enrich the diversity of crack samples. Extensive experiments conducted on the Crack500, DeepCrack, and CFD datasets demonstrate that the proposed CAC significantly outperforms other WSCS methods.

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A deeper generative adversarial network for grooved cement concrete pavement crack detection

Cited by 40 publications

References 24 publications

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

A robust self-supervised approach for fine-grained crack detection in concrete structures

CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation

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