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Shuffle Attention-Based Pavement-Sealed Crack Distress Detection
by
Yuan, Bo
, Li, Wei
, Zhao, Kaiyue
, Pei, Lili
, Sun, Zhaoyun
in
Automation
/ Concrete pavements
/ Cracks
/ Datasets
/ Deep learning
/ distress detection
/ neural network
/ pavement-sealed crack
/ shuffle attention
/ Unmanned aerial vehicles
/ wise intersection over union
2024
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Shuffle Attention-Based Pavement-Sealed Crack Distress Detection
by
Yuan, Bo
, Li, Wei
, Zhao, Kaiyue
, Pei, Lili
, Sun, Zhaoyun
in
Automation
/ Concrete pavements
/ Cracks
/ Datasets
/ Deep learning
/ distress detection
/ neural network
/ pavement-sealed crack
/ shuffle attention
/ Unmanned aerial vehicles
/ wise intersection over union
2024
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Do you wish to request the book?
Shuffle Attention-Based Pavement-Sealed Crack Distress Detection
by
Yuan, Bo
, Li, Wei
, Zhao, Kaiyue
, Pei, Lili
, Sun, Zhaoyun
in
Automation
/ Concrete pavements
/ Cracks
/ Datasets
/ Deep learning
/ distress detection
/ neural network
/ pavement-sealed crack
/ shuffle attention
/ Unmanned aerial vehicles
/ wise intersection over union
2024
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Shuffle Attention-Based Pavement-Sealed Crack Distress Detection
Journal Article
Shuffle Attention-Based Pavement-Sealed Crack Distress Detection
2024
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Overview
To enhance the detection of pavement-sealed cracks and ensure the long-term stability of pavement performance, a novel approach called the shuffle attention-based pavement-sealed crack detection is proposed. This method consists of three essential components: the feature extraction network, the detection head, and the Wise Intersection over Union loss function. Within both the feature extraction network and the detection head, the shuffle attention module is integrated to capture the high-dimensional semantic information of pavement-sealed cracks by combining spatial and channel attention in parallel. The two-way detection head with multi-scale feature fusion efficiently combines contextual information for pavement-sealed crack detection. Additionally, the Wise Intersection over Union loss function dynamically adjusts the gradient gain, enhancing the accuracy of bounding box fitting and coverage area. Experimental results highlight the superiority of our proposed method, with higher mAP@0.5 (98.02%), Recall (0.9768), and F1-score (0.9680) values compared to the one-stage state-of-the-art methods, showcasing improvements of 0.81%, 1.8%, and 2.79%, respectively.
Publisher
MDPI AG
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