• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Road Damage Detection and Classification based on Multi-level Feature Pyramids

Vol. 15, No. 2, February 28, 2021
10.3837/tiis.2021.02.022, Download Paper (Free):

Abstract

Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.


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Cite this article

[IEEE Style]
J. Yin, J. Qu, W. Huang and Q. Chen, "Road Damage Detection and Classification based on Multi-level Feature Pyramids," KSII Transactions on Internet and Information Systems, vol. 15, no. 2, pp. 786-799, 2021. DOI: 10.3837/tiis.2021.02.022.

[ACM Style]
Junru Yin, Jiantao Qu, Wei Huang, and Qiqiang Chen. 2021. Road Damage Detection and Classification based on Multi-level Feature Pyramids. KSII Transactions on Internet and Information Systems, 15, 2, (2021), 786-799. DOI: 10.3837/tiis.2021.02.022.