What question did this study set out to answer?

To develop a deep learning framework capable of accurately detecting, classifying, and generating segmentation masks for road damage under complex background conditions.

January 1, 2020Computers, materials & continua/Computers, materials & continua (Print)Open Access

Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

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Authors

QCQiqiang ChenSoutheast University XGXinxin GanSecond Military Medical University WHWei HuangFujian Normal University

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Implication

Experimental study demonstrates enhanced road damage detection and segmentation in pavement imagery, indicating improved automated infrastructure maintenance.

Key Points

To develop a deep learning framework capable of accurately detecting, classifying, and generating segmentation masks for road damage under complex background conditions.
Adapted a Mask R-CNN framework using densely connected convolutional networks (DenseNet) as the feature extraction backbone.
Integrated a feature pyramid network (FPN) for multi-scale feature combination and a region proposal network (RPN) to generate candidate road damage areas.
Applied a fully convolutional neural network branch to classify damage types, refine bounding boxes, and construct pixel-level damage masks.
Enabled simultaneous detection, categorization, and pixel-level mask generation for irregular road damage.
Achieved superior detection and classification performance compared with existing baseline methods (quantitative metrics not reported in the abstract).

Cite This Study

Chen et al. (2020) studied this question.

synapsesocial.com/papers/6a867252e3ff5bf5642ed38c https://doi.org/10.32604/cmc.2020.011191

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Implication

Experimental study demonstrates enhanced road damage detection and segmentation in pavement imagery, indicating improved automated infrastructure maintenance.

Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

Discussion

Member takes

Implication

Key Points

Cite This Study

Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

Discussion

Member takes

Implication

Key Points

Cite This Study

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